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Perspective article, unprecedented climate change in india and a three-pronged method for reliable weather and climate prediction.

• 1 National Institute for Space Research, São José dos Campos, Brazil
• 2 Department of Meteorology and Oceanography, Andhra University, Visakhapatnam, India
• 3 Centre for Earth, Ocean and Atmospheric Sciences, University of Hyderabad, Hyderabad, India

India, one of the most disaster-prone countries in the world, has suffered severe economic losses as well as life losses as per the World Focus report. 1 More than 80% of its land and more than 50 million of its people are affected by weather disasters. Disaster mitigation necessitates reliable future predictions, which need focused climate change research. From the climate change perspective, the summer monsoon, the main lifeline of India, is predicted to change very adversely. The duration of the rainy season is going to shrink, and pre-monsoon drying can also occur. These future changes can impact the increase of vector-borne diseases, such as malaria, dengue, and others. In another recent study, 29 world experts from various institutions found that the largest exposure to disasters, such as tropical cyclones (TCs), river floods, droughts, and heat waves, is over India. For improved and skillful prediction, we suggest a three-stage cumulative method, namely, K is for observational analysis, U is for knowledge and understanding, and M is for modeling and prediction. In this brief note, we report our perspective of imminent weather disasters to India, namely, monsoons and TCs, and how the weather and climate forecasting can be improved, leading to better climate change adaptation.

Introduction

The Indian economy still significantly depends on agriculture, which, in turn, depends on the summer monsoon rains occurring from June to September. In the present scenario of climate change, it is essential to know how the Indian summer monsoon rainfall is going to change in the future. In a recent detailed study with regional climate model projections, Ashfaq et al. (2020) suggest that an important adverse signal of future climate change over the Indian monsoon region in the RCP8.5 scenario ( Krishnan et al., 2020 ; Jyoteeshkumar Reddy et al., 2021 ) can occur. The sinking of the Indian monsoon rainy season onset is projected to delay by five to eight pentads and a shrinking of the monsoon rainy season. India can experience pre-monsoon drying as well.

In a recent innovative study, 29 world experts ( Lange et al., 2020 ) from different institutions and different countries, reached some important conclusions. These inferences deserve urgent attention and action plans by policymakers. They considered six categories of extreme climate impact events, namely, river floods, cyclones, crop failures, wildfires, heat waves, and droughts. These authors ( Lange et al., 2020 ) quantified the pure effect of climate change on the exposure of the global population to the events mentioned. One important conclusion, which is of grave concern to India, is that the largest increase in exposure is projected here. Thus, to avoid huge damages due to these disasters, such as deaths and loss of property, urgent and more reliable predictions are needed. We, however, must clarify that there has been tremendous improvement in numerical prediction of tropical cyclones (TCs) in the last few decades in India [e.g., Pattanaik and Mohapatra, 2021 ; Saranya Ganesh et al., 2021 ; Sarkar et al., 2021 , and all other papers in January 2021 of Mausam, a special issue on the state of the art on TC prediction in the North Indian Ocean (NIO)], but what we claim is that applying theory can enhance the skills from the current day model outputs substantially more as discussed in the following section. To provide an analogy, in a recent study, Rao et al. (2021) attempted to connect observations, theory, and a prediction plan for heat waves. This prediction method can be applied to a numerical weather prediction model to predict deadly heat waves; thus, Rao et al. (2021) used a K, U, and M approach for the prediction of deadly heat waves over India.

From the context of the three-pronged K, U, and M method (hereafter, KUM), there are sufficient observational studies, or K, and also some attempts have been made using highly sophisticated, state-of-the-art (atmosphere and ocean) coupled models for predictions, M. What is most lacking, however, are theoretical studies (U) aiming to find out the causes for disastrous TCs or the highly complex regional monsoons.

According to a recent 2021 overview of current research results by the Geophysical Fluid Dynamics Laboratory of Global Warming and Hurricanes 2 , the severity and frequency of TCs are increasing globally. A recent study ( Balaguru et al., 2015 ) also suggests an increase of TCs globally even over the NIO. Essentially, the increase in the strong TCs has far-reaching implications for society because these include the most harmful aspects, namely, storm surges and heavy rains with intense wind speeds. Indeed, TC rainfall rates will possibly increase in the future due to various anthropogenic effects and accompanying increases in atmospheric moisture. Rapid intensification of TCs poses forecast challenges and increased risks for coastal communities ( Emanuel, 2017 ). Recent modeling studies ( Emanuel, 2020 ) show an increase of 10–15% for precipitation rates averaged within about 100 km of the cyclone for a 2°C global warming scenario. As per IPCC AR5, higher levels of coastal flooding due to TCs are expected to occur, all else assumed to be constant due to rising sea levels. In this situation, together with the rise in sea level, the impact due to the strong TCs deteriorates the conditions of the increasing coastal population across India and the neighborhood. As the NIO is one of the typical regions with a population of 1.353 billion (2018), about 18% of the global population by 2020, it is highly susceptible to strong TCs causing adverse living conditions, and the implication is that stronger TCs will be worse.

According to reports from a respected BBC newspaper 3 , 4 , and a potential report 5 from the Indian Meteorological Department, Amphan is a very severe cyclone that transited the west coast of India in 2020 and also caused a lot of damage. The super cyclonic storm Amphan is the costliest case in the recorded history of TCs with damage of US$15.78 billion and also total fatalities of 269. Similarly, in the year 2019, a loss of US$11 billion occurred due to TCs. In the year 2020, there was a record-breaking occurrence of eight TCs over the NIO: five cyclones and three major cyclones compared to the climatology of 4.9, 1.5, and 0.7. We note a drastic increase in category 3 and beyond hurricanes occurring in the NIO and also a significant increase in the Northern and Southern hemispheres ( Figure 1 ). Also, there is a substantial increase in accumulated cyclone energy (ACE) in the last two decades in the NIO and Northern and Southern hemispheres ( Figure 2 ). In 2019, record-breaking ACE of 85 × 10 4 knots 2 , occurred in the NIO, nearly twice the previous record ( Singh et al., 2021 ; Wang et al., 2021 , BAMS). The decrease in the projected number of TCs found in some studies ( Sugi et al., 2017 ) is overcompensated by the huge increase in intensity similar to that found over the NIO in 2019 and 2020. Furthermore, as if to worsen the situation in a colloquial sense, Wang and Murakami (2020) show that the general atmospheric and ocean parameters, which show a high global correlation with the number of TCs, nevertheless show only a very low correlation with TCs of the NIO. Thus, urgent research should be carried out to understand the causes of the occurrence of TCs over the NIO. Even globally, in the last 39 years (1980–2018), weather disasters caused about 23,000 fatalities and US$100 billion in damages worldwide. Each year, weather events displace huge populations, drive people into poverty, and dampen economic growth globally ( Kousky, 2014 ; Munich, 2020 ; Hoegh-Guldberg et al., in press ). The underlying causes show a marked signal of anthropogenic roots and global warming (e.g., Sobel et al., 2016 ; Im et al., 2017 ).

Figure 1 . The number of category 3+ hurricanes that occurred in the Northern and Southern hemispheres and the NIO (black dotted line indicates a linear trend, and orange line indicates significance at the 95% confidence level) ( http://tropical.atmos.colostate.edu/Realtime/index.php?archandloc=northindian ).

Figure 2 . ACE (in 10 4 Knots 2 ) in the Northern and Southern hemispheres and the NIO (black dotted line indicates a linear trend, and orange line indicates significance at 95% confidence level) ( http://tropical.atmos.colostate.edu/Realtime/index.php?archandloc=northindian ).

Henceforth, we focus on the TCs as well as summer monsoons, which are the two most relevant weather and climate phenomena for the Indian region.

A Three-Stage Method to Study and Plan Reliable Prediction

Because India is rigorously prone to natural disasters as well as impacts due to anticipated changes in the summer monsoon in the future, there is indeed a serious question as to how to study the causal mechanisms of these disasters and plan to mitigate them. In this context, the late Gill (1985) , an accomplished geosciences expert, suggested almost 35 years ago the KUM method, namely, knowledge, understanding, and modeling, a three-pronged approach. The first step (K) is to improve observational knowledge of calamity-causing weather events and next a theoretical understanding to find out the cause of a specific effect, probably utilizing linear analytical mathematical solutions (U). Finally, the third one (M), using the presently available highly complex coupled (atmosphere and ocean) models giving numerical solutions to non-linear equations, pioneered by Phillips (1956) , predicting future occurrences. The order of KUM seems to be important. Although relatively substantial observational results are available in the Indian context for meteorological and oceanographic events, very few theoretical studies have been made delineating the causal mechanisms. Thus, this aspect should be given priority. In a recent comment, Emanuel (2020) also stressed the need for theoretical studies. Finally, only after acquiring the observational, knowledge, and cause-and-effect relationships in theoretical studies, only then , should one embark on numerical or climate modeling to successfully predict the future.

In this context, it is illuminating to recall the comments of Phillips (1970) , one of the founding fathers of theoretical meteorology and numerical weather prediction: “in making a numerical forecast, one takes a set of numbers.regardless of.synoptic structures.by another set of numbers, representing the forecast. The computation of a set of numbers depicting the formation of a front, is of course, not a theory of fronts (unless one is content to point to the equation of motion as theory!!!!!)” Thus, one should be very careful using numerical models to develop a theory of TCs, and in the Indian context, monsoon depressions (MDs) are crucial for monsoon rainfall. Today, many students and scientists worldwide spend most of their valuable time dealing with huge data sets and running numerical models to simulate rather than to develop a theory. Tellingly, Emanuel (2020) , mentions that presently there is “computing too much and thinking too little.” Indeed, there is an urgent need for curiosity-driven theoretical research even in the Indian context. One interesting example to stress the importance of theory is, today, that the best numerical weather prediction is in mid and high latitudes in winter. This is because the basic theory behind the mechanism of winter weather changes, the baroclinic instability, was discovered more than 70 years ago by Charney (1947) , and models and observations evolved accordingly. Thus, it is important to realize, without the correct understanding of the causal mechanisms through theory, one will never be able to predict correctly and completely the required weather or climate or its changes with just the brute force of computers available today!!!

TCs Over the NIO

Regarding the theory of the generation mechanisms of TCs, there are two well-known hypotheses, namely, (a) the conditional instability of the second kind (CISK) and (b) wind-induced surface heat exchange (WISHE) (please refer to Tomassini, 2020 for a comprehensive discussion of these two processes). A detailed discussion of these two is beyond the scope of the present short article. However, the authors quickly discuss these two mechanisms in the context of TCs over NIO.

In the case of TCs, the pre-synoptic disturbances get their energy by the complex interaction of two different horizontal scales, namely, cumulus convection of about 1 km and synoptic systems of about 500 km. How this interaction happens is a topic of debate, though, and most of the research in the published literature is about TCs in tropical ocean basins other than the NIO region.

Briefly, we discuss the basic characteristics of CISK and quasi-equilibrium (or WISHE). In the process of CISK, the buoyant convection can occur only when low-level stability is weakened (see Figure 2 ; Ooyama, 1969 ), and in the other, moist convection is governed by the vertically integrated measure of instability. As noted by Tomassini (2020) , meteorological conditions vary greatly from one region to the other in the tropics and also in the same region from one season to another (see Ashok et al., 2000 ; Rao et al., 2000 ; Raymond et al., 2015 ). Raymond mentions two tropical places, Sahel and the Western Pacific, where conditions are very different. Now, how do the conditions vary, during (i) pre-monsoon, (ii) MDs, and (iii) post-monsoon TCs? Similar to Bony et al. (2017) , we suggest that more detailed observations of both satellite measurements and data developed in field programs should be used to understand the convection and circulation coupling of TCs over NIO. For example, the INCOMPASS IOP field program, which collects data from strategically installed ground-based instruments in India, is one such program ( Fletcher et al., 2018 ).

Another, synoptic disturbance of importance is a MD. Despite several observational and theoretical studies by many authors (for example, Sikka, 1977 ; Mishra and Salvekar, 1979 ; Aravequia et al., 1995 ; Boos et al., 2017 ) trying to understand the basic mechanism of origin, some fundamental questions remain unanswered. Similar to TCs, the lack of understanding of how convection and MD circulation couple hinders the prediction. For both TCs and MDs, we suggest analyzing time vertical sections of potential temperature, equivalent potential temperature, and saturated equivalent potential temperature such that one can get an idea of the relative importance of CISK or the quasi-equilibrium hypothesis discussed briefly above.

Another method for elucidating the study is to examine the system's energetics, i.e., TCs or MDs. Lorenz (1960) mentions, “one enlightening method of studying the behaviour of the atmosphere, or a portion of it, consists of examining the behaviour of the energy involved.” Earlier Mishra and Rao (2001) used limited area energetics to infer the mechanism of generation of Northeast Brazil's upper tropospheric vortices. Also, Rao and Rajamani (1972) examined the energetics of MDs. These methods of energy analysis, for example, can be used to isolate or single out the basic mechanism of generation of TCs or MDs, using more recent well-covered data, such as the INCOMPASS IOP program ( Turner et al., 2019 ). Later, targeted numerical model studies should be used to not only verify the process/processes identified in energetic and diagnostic studies, but to design dynamics-based indices related to TC formation that are relatively easier to predict. For example, a CISK parameter may be easier to predict with a longer lead as compared with the TC rainfall. These methods are again akin to the KUM approach. Such carefully verified and designed indices, when operationalized, will substantially help in extending the lead prediction time. Probabilistic dynamical-statistical downscaling tools can also be developed to relate local rainfall with these indices. This will also potentially enhance the lead time of the TC-related deluge. Similarly, a better understanding of model ability in capturing the conversions between different forms of energy.

Again, several aspects of monsoons, particularly, the Indian Monsoon are still not completely clear and hinder the mechanisms of prediction. In a recent exhaustive study, Geen et al. (2020) , discussed several aspects, primarily from a theoretical standpoint even though this study was developed based on the concept of a global monsoon, Figure 2 of Geen et al. (2020) shows only a very low correlation in interannual variations of rainfall, the main meteorological element that must be predicted. However, the different regions of monsoons with different geographical boundaries raise serious objections about the global monsoon concept.

Several studies exist in the literature regarding the observed aspects of the Indian summer monsoon (the K part of the three-pronged method), and modern numerical models are employed to improve prediction skills ( Sahai et al., 2016 ; Rao et al., 2019 ; Mohanty et al., 2020 ). From an almost zero skill, we have reached a stage at which the skills for predicting the area-averaged Indian summer monsoon are found to be statistically significant. This is great progress. Having said that, there is a great scope for further improvement. Although the broad regionally averaged skills are statistically significant, they are modest. Further, improving the skills such that they are locally useful is the obvious goal but still a long way ahead. Although the prediction skill improved through better methods of, for example, data assimilation and parametrization schemes, to improve the predictions further, we need to diagnose the improved representation (e.g., Halder et al., 2016 ; Saha et al., 2019 ; Hazra et al., 2020 ), better replication of physical processes and scale interactions.

Notwithstanding all these technical improvements, the large-scale physical causal mechanisms are not clear yet. This can only be done with the studies aiming to understand the cause-and-effect relation or the U in the three-pronged method. As mentioned earlier, with more observational studies aiming to identify the correct interaction mechanism over NIO between convection and large-scale monsoon circulation (either CISK or WHISE), then this mechanism can be included in the numerical models. Also, controlled experiments using simple models, such as the one by Rao et al. (2000) , can be used to identify relative roles of mountains and thermal contrast in generating the Indian summer monsoon. In the state-of-the-art coupled models, because of extremely complex non-linear interactions among various physical mechanisms, it is almost impossible to isolate the cause of a specific effect.

Again, the diagnostic study based on energetics, such as the generation of available potential energy (PE) by latent heat and the baroclinic conversions, for example, may reveal relative roles of some physical processes, such as convection in the Indian monsoon. In a recent companion study (Rao et al., under review), comparing the South American and Indian monsoons, we found that, in the Indian monsoon, the baroclinic conversions P ¯ (mean available PE) to P ′ (eddy PE) to kinetic energy (KE) is non-existent, and the KE of monsoon is mainly furnished by the generation of perturbation PE by latent heating (rainfall) and subsequent conversion to KE. In contrast, over the South American monsoon, both the baroclinic conversions and generation terms are equally important. This is probably because the Himalayas extend from East to West across the cardinal northern border of the country, which does not allow mid-latitude baroclinic waves to penetrate at lower levels while the Andes mountains in South America extend along North to South, permit these waves to penetrate even as low latitude as Manaus, where even austral summer cold waves (FRAIAGENS) are noted. Furthermore, studies are necessary to verify how energetics vary between wet and dry monsoons in these two regions.

In a review article by Geen et al. (2020) , the authors discuss attempts to understand fundamental dynamics (U in our three-pronged method). Geen et al. (2020) mention a very similar KUM approach for monsoons (their section 3). Such efforts are urgently needed from the context of the Indian monsoon. They even discuss the south Asian monsoon (their section 3.1.2). Although they tried to reconcile between global and regional monsoon features, the differences are more striking as we mentioned earlier, regarding the Indian and South American monsoons. In the case of the East Asian monsoon, at least one author ( Molnar et al., 2010 ) mentions, “‘monsoon' is somewhat of a misnomer.”

Although there are some uncertainties in the methods used by Lange et al. (2020) , the importance of their conclusion is unambiguous. They mention that “anthropogenic” climate change has already substantially increased the exposure to extreme global climatic impacts, and anthropogenic warming is projected to exacerbate the pattern of climate change that we are already noticing nowadays. Thus, it is urgent to restrain the increase in global average temperature well below 2°C, which would significantly reduce the risks and impacts of climate change 6 ( Benitez, 2009 ; Dash et al., 2013 ). All this, therefore, underscores the urgency for climate action expressed in the Paris agreement of 2015. Even in a climate change context, using the KUM approach will help in a better diagnosis of the changes in regional implications for large-scale instabilities to diabatic processes. These can help in design model-based indices that can inform the stakeholders working on climate change mitigation and adaptation.

Recommendations

We are in an era in which observational data availability in the tropics has improved significantly and is going to be further improved. In this context, it is recommended that the forecasters and researchers of Indian weather and climate use this excellent opportunity to build theoretical knowledge unique to the regional weather and climate. The knowledge gained should be translated to identify tangible, large-scale dynamical process indices. Such indices will be very useful to extend the lead prediction skills of important weather and climate phenomenon, such as TCs, MDs, etc. Similarly, (i) evaluating the model capacity in predicting and calibration of association between hindcast perturbation PE, latent heating, and subsequent conversion to KE, and (ii) comparing the observations will potentially provide us with indices that can be directly used to predict subseasonal monsoonal rainfall with longer leads. The above recommendations are just examples. In summary, identifying the key dynamics behind important weather and climate processes at discernible time scales and designing useful dynamical indices that can be used to extend the lead forecast envelope will be the way forward.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://tropical.atmos.colostate.edu .

Author Contributions

VB conceived the idea. VB wrote the manuscript with inputs from KA and using the results from DG analysis. KA comprehensively revised the article. All authors contributed to the article and approved the submitted version.

The publication charge of this article is fully funded by the Frontiers in Climate Journal.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors thank Prof. Matthew Collins, Specialty Chief Editor, Frontiers in Climate Journal, and reviewers for their helpful feedback and recommendations in improving the manuscript quality. The authors are grateful to the Frontiers in Climate Journal Committee for waiving the article's publishing fees. We thank the reviewers for their critical comments, which helped to improve the quality of the article.

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2. ^ https://www.gfdl.noaa.gov/global-warming-and-hurricanes/

3. ^ https://www.bbc.com/news/world-asia-india-52749935

4. ^ https://en.wikipedia.org/wiki/2020_North_Indian_Ocean_cyclone_season

5. ^ https://mausam.imd.gov.in/Forecast/marquee_data/indian111.pdf

6. ^ https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement

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Keywords: KUM method, extreme weather, human suffering, tropical cyclone, monsoon, Indian summer monsoon (ISM)

Citation: Brahmananda Rao V, Ashok K and Govardhan D (2021) Unprecedented Climate Change in India and a Three-Pronged Method for Reliable Weather and Climate Prediction. Front. Clim. 3:716507. doi: 10.3389/fclim.2021.716507

Received: 28 May 2021; Accepted: 04 October 2021; Published: 15 November 2021.

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Copyright © 2021 Brahmananda Rao, Ashok and Govardhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Vadlamudi Brahmananda Rao, raovadlamud@gmail.com orcid.org/0000-0001-5905-9806

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Impacts of Global Warming on Ecology and Meteorology and the Related Physical Mechanisms, Evaluation and Prediction

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• Published: 25 June 2020

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches

• Bushra Praveen 1 ,
• Swapan Talukdar 2 ,
• Shahfahad 3 ,
• Susanta Mahato 2 ,
• Jayanta Mondal 2 ,
• Pritee Sharma 1 ,
• Abu Reza Md. Towfiqul Islam 4 &
• Atiqur Rahman 3  

Scientific Reports volume  10 , Article number:  10342 ( 2020 ) Cite this article

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This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen’s Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (−8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901–1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions’ also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.

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Introduction

Rainfall is a key part of hydrological cycle and alteration of its pattern directly affect the water resources 1 . The changing pattern of rainfall in consequence of climate change is now concerning issues to water resource managers and hydrologists 2 . Srivastava et al . 3 and Islam et al . 4 reported that the changes of rainfall quantities and frequencies directly changing the stream flow pattern and its demand, spatiotemporal allocation of run-off, ground water reserves and soil moisture. Consequently, these changes showed the widespread consequences on the water resource, environment, terrestrial ecosystem, ocean, bio-diversity, agricultural and food security. The drought and flood like hazardous events can be occurred frequently because of the extreme changes of rainfall trend 5 . Gupta et al . 6 documented that the amount of soil moisture for crop production is totally determined by the amount of rainfall. The monsoon rainfall plays a vital role for agriculture in India. 68% of cultivated land to the total cultivated land of India is occupying by the rain fed agriculture which supports 60% of livestock population and 40% of human population 7 . Hence, the research on the climate change or most specifically on the changes of rainfall occurrences and its allocation are the most significant way for sustainable water resource management. Therefore, the sustainable development of agriculture in India requires the noteworthy research on the identification and quantification of climate change 7 . Most importantly, a complete understanding of the precipitation pattern in the changing environment will help in better decision making and improve the adapting-capacity of the communities to sustain the extreme weather events.

Nowadays, the water resources have been considered as the key concern for any kinds of development program and planning which includes effective water resource management, food production sector and flood control. The uneven allocation of water supply throughout the country, because of the natural pattern of rainfall occurrence which varies significantly in space and time, is the main hindrance for the effective water resource management in India 8 . The climate change further accelerates this rainfall variability 7 . Consequently, many regions of the country receive huge amounts of rainfall during the monsoon, while others receive very less amount of rainfall and frequently experience the worst reality of water scarcity. Furthermore, the worst consequence of climate change, especially rainfall change, has been experiencing by the agricultural sector of this country. Because the rainfall has not been taking place when it is expected and vice versa 9 . Even, the high winds and hails have been frequently occurred. Consequently, enormous losses to crops have been taken place and the farmers who totally dependent on the agriculture have become devastated 7 . The freakish weather is causing havoc on the farmers community, as a results, the farmers have gone extreme to the committing of suicide. During the period 1995–2014, 300000 farmers have committed suicide in India 10 . Hence, it is noteworthy to evaluate whether there is any trend in rainfall and any pattern in variability 11 .

Therefore, to explore the variability and changes in pattern and existence of trend in rainfall over different spatial horizons have been the key aspects in the study of hydrology, climatology and meteorology worldwide 12 , 13 , 14 , 15 , 16 , 17 . In most studies, researchers have used parametric and non-parametric methods 18 like regression test 19 , 20 , Mann-Kendall test 21 , 22 , Kendall rank correlation test 23 , Sen’s slope estimation 24 , 25 and Spearman rank correlation test 26 . In the present study, the non-parametric test like Mann-Kendall test was applied to detect the trend in rainfall as it is one of the most often applied global methods for trend detection in hydrology, climatology and meteorology 27 , 28 , 29 , 30 , 31 . One of the major reason to use non-parametric tests in the present study is these can be used on independent time series data and are also not much sensitive to outliers 32 . However, the change detection analysis in the climatologic and hydrological data series is the important aspect for the trend analysis throughout the world 33 , 34 .The change detection methods include the Standard Normal Homogeneity Test (SNHT) 35 , Buishand range test 36 and the Pettitt’s test 37 . The trend analysis was carried out before the change point analysis in many researches 32 . This approach can lead to misleading results as the information obtained from change point detection analysis has not been considered for the trend analysis 38 . Hence, Li et al . 36 highly recommended performing the change detection analysis first followed by the trend analysis. The results obtained from this approach are more reasonable and reliable.

In India, several attempts have already been done in the past to detect rainfall trends in regional and national levels 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 . The research on trend analysis has believed to be a useful tool as it provides the significant information about the possibility of the future changes 4 , 47 , 48 . However, multiple studies have conducted trend analysis using MK test and reported that India as a whole has not recorded significant trend of increase or decrease in annual average rainfall 7 , 46 , 49 . Many researchers identified significant trend in long-term rainfall in India 2 , 44 , 50 , 51 , 52 , 53 , while the significant long-term trend has not been detected in monsoon rainfall on a national scale 7 . Patra et al . 54 studied the trend analysis in monthly, seasonal and annual rainfall over Orissa and reported that long-term insignificant negative trend was detected in annual and monsoonal rainfall, while the positive trend was detected in post- monsoon season. But to the best of authors knowledge, most of the researchers applied non-parametric tests to detect and followed by trend analysis. Therefore, to get reliable and reasonable results of trend, we have obtained both approaches like trend analysis before change point and first change point analysis followed by trend detection.

Sen 55 developed the innovative trend analysis that has been utilized for detecting trend in meteorological, hydrological and environmental variables 56 , 57 , 58 , 59 , 60 . The innovative trend analysis has the worldwide applicability over the non-parametric approach 61 like Mann-Kendal test, spearman’s correlation test and Sen’s slope estimation because these non-parametric test are very much sensitive to the distribution assumptions, serial correlation, size of the time series data and seasonal cycle 62 . Von Storch 63 stated that if the data series have statistically significant serial correlation, the MK test can generate no noteworthy trend existence in the data series. Sen 55 stated that the effective, efficient and optimum water resource management needs to identify the trend not only monotonically over time, but also needs to identify trends separately which have the high, medium and low value which can be possible to identify by the innovative trend. In addition, the innovative trend is not sensitive to serial correlation, non-linearity, and size of the time series data and gives the robust and powerful result with less error. Therefore, researchers prefer the innovative trend analysis for detecting trend in hydrological, meteorological studies 18 , 44 , 55 , 64 , 65 . Therefore, in the present study, apart from Mann-Kendall test, the innovative trend was also utilized for detecting trend. In India, very few studies were conducted to detect trend using innovative trend analysis. But to the best of authors’ knowledge, the application of innovative trend to detect seasonal rainfall for whole India would be the first application.

As we have considered detecting change point using MK test before change point analysis, MK test after change point analysis and innovative trend analysis, the planners of water resource management, environmentalist, and hydrologists can have the chance to compare the result of all these techniques and get high precision trend information in annual and seasonal rainfall. Therefore, in this line of thinking, we can conclude that the study has the quite novelty.

The trend analysis facilitates to understand the present and past climatic changes, but the future forecasting is more useful for the planners to execute the proper planning taking into account future changes in climatic variables 66 . To project future information of the climatic variables, in addition to the complicated climate model which works on global scale, regional forecasting could be carried using statistical techniques and machine learning soft computing techniques 67 , 68 . To execute the physical models, large numbers of database, high configured system, advanced technology, technical expert are required. The physical models are both time and money consuming, although they provide highly reliable results. The statistical techniques like auto regressive (AR), moving average (MA), auto regressive moving average (ARMA) and auto regressive integrated moving average (ARIMA) have several limitations such as AR model regresses past values, while MA model utilizes past error as the explanatory variables and ARMA model can perform for stationary time series data 67 . However, recently, the application of artificial intelligence (AI) models like machine learning techniques have gained attention 69 . Unlike physical models, the AI models perform very well as it does not require huge information, but can handle huge and complicated data sets if provided 70 . It can perform in non-stationary time series data 71 . Darji et al . 67 conducted review survey regarding the rainfall forecasting using neural networks and reported that back propagation based neural network performs well to forecast rainfall. In the present study, we utilized multilayer perceptron (MLP) algorithm based neural network for predicting the rainfall. The MLP based ANN works using back propagation algorithm. However, the application of MLP based artificial neural network (ANN) for predicting and forecasting of multifaceted hydrological and climatic phenomena and has gained popularity across the globe as it provides reliable results 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 . The novelty of the present study is that the application of MLP-ANN is not concentrated on the one or more states or overall India rather the present study considers predicting and forecasting rainfall for thirty-four meteorological sub-divisions of India. Therefore, the study will be highly beneficial as it worked on the whole India in micro level.

Therefore, based on the previous literatures, research gaps, the objectives of the present study are: (1) to analyze the variability and trend analysis in the overall annual and seasonal rainfall for thirty-four meteorological sub-divisions; (2) to detect the change point for all sub-divisions; (3) to explore the change point wise rainfall variability and trend analysis; (4) to detect trend using innovative trend analysis; (5) to predict and forecast rainfall up to 2030 for all sub-divisions; (6) to investigate the causes of the changes of rainfall pattern.

Material and Methods

Study area and data source.

The geographical location of India is between 8.4° and 37.6°N latitude and 68.7° and 97.25°E longitude. The whole India can be categorized into four homogenous climatic regions such as North-West India (NW India), North-East India (NE India), Central India and Peninsular India on the basis of the distribution and occurrence pattern of rainfall and temperature 46 . Although the whole country experiences almost all types of climate due to its physiographic location 59 . However, India observes the four seasons, like summer season (January-February), winter (March-May), monsoon (June-September) and post-monsoon (October-December) 82 . The India receives 117 cm rainfall, out of which 80% of rainfall is observed during monsoon month.

The whole Indian region has been sub-divided into thirty-four meteorological subdivisions by the Indian Meteorological Department (IMD) where the climatic parameters like rainfall, temperature, wind speed, humidity have been recorded since 1901 (Fig.  1a ). In this study, we obtained rainfall data for 115 years (1901–2015) from thirty-four meteorological sub-divisions. The collected data was continuous in nature with no missing values.

( a ) Geographical location of the study area; LOWESS curve on annual and seasonal rainfall where figure shows ( b ) annual rainfall, ( c ) winter ( d ) summer ( e ) monsoon and ( f ) post monsoon rainfall.

Method for trend analysis

In this paper we used the Mann-Kendal test 18 , a non-parametric statistical test based on rank system, to detect the trend in long-term rainfall data series. The MK test is mainly used for detecting trend in hydro-climatic data series as the lower sensitivity to any sudden change 83 , 84 . To perform this test, it is essential to evaluate the presence of serial correlation within the data series 85 . A positive serial correlation can support the expected number of bogus positive products in the MK test 86 . For this reason, the serial correlation must be excluded prior to applying the MK test. To eliminate the serial correlation, the trend free pre-whitening (TFPW) technique proposed by Yue and Wang 85 was used.

The MK test was calculated using Eq.  1 :

In a time series, K i , i  =  1, 2, 3, ……….n , the value of S is supposed to be similar as the normal distribution with a mean 0 and while the discrepancy of statistics S has been computed using Eq.  2 :

The Z MK value is used to find out that the time series information is demonstrating a significant trend or not. The Z MK value is computed using Eq.  3 :

The positive and negative values of Z in a normalized test statistic reflect the increasing and decreasing trend, respectively, while the Z having 0 values reflects a normal distributed data series.

Method for change point detection

In the present study, we utilized Pettitt test proposed by Pettitt 37 and Standard Normal Homogeneity test (SNHT) developed by Alexandersson and Moberg 35 to explore the presence of the abruptly shifting change points in the time series annual and seasonal rainfall dataset for all meteorological sub-divisions.

Pettitt Test

The Pettitt test is a distribution-free rank based test, used to discover noteworthy changes in the mean of the time series. It is more helpful when the hypothesis testing about location of a change point is not necessary. This test has been used extensively to identify the changes observed in climatic and hydrological data series 87 , 88 . When length of a time series is represented by t and the shift take place at m years, the consequential test statistics are expressed as given in Eq. ( 4 ).The statistic is similar to the Mann- Whitney statistic, which characterized by two samples, such as k1, k2…, km and km+1, k2…, kn:

where \(\mathrm{sgn}\) in Eq.  4 is defined by Eq.  5 :

The test statistic Ut,m is calculated from all haphazard variables from 1 to n . The majority of distinctive change points are recognized at the point where the magnitude of the test statistic | Ut,m  | is highest (Eq.  6 ).

The probability of shifting year is estimated when | Ut,m  | is maximum following Eq.  7 :

If the p- value is less than the significance level α, the null hypothesis is considered to be rejected.

Standard Normal Homogeneity Test (SNHT)

The standard normal homogeneity test is also known as the Alexanderson test. This test is applied to detect sudden shift or presence of change point in time series of climatic and hydrologic datasets. The change point has been detected following Eq.  8 :

The change point refers to the point, when T s attains maximum value in the data series. The T m is derived using Eq.  9 :

where \(\bar{m}\) represents the mean and s represents the standard deviation of the sample data.

Buishand Rang Test

The Buishand range test is also called as Cumulative Deviation test, which is calculated based on the adjusted biased sums or cumulative deviation from mean. The change point using this test is detected following Eqs.  11 and 12 :

The \(S/\sqrt{n}\) is then estimated using the critical values proposed by Buishand 38 .

Methods for innovative trend analysis

The innovative trend analysis, proposed by Şen 55 , was applied to detect the trend in long-term time series rainfall data of India. The innovative trend analysis has greatest advantages over the MK test and other parametric and non-parametric statistical tests, which is that it does not need any assumptions like non-linearity, serial correlation and sample numbers. However, the graph of this test is being plotted on a Cartesian coordinate system depending on a sub-section time series. As per the method’s requirement, the time series rainfall data has been sub-divided in two time series data sets: 1901–1957 and 1958–2015. Both segments were fixed in an ascending order. Typically the first sub-series (x i ) was represented on x axis, while the other sub-series (y i ) was represented on y axis. The data is plotted on the 1: 1 line. If the data is plotted along the 1:1 line, it indicates simply no trend within the time series. If the data is being plotted above the 1:1 line, it will state that the moment series shows an increasing trend, and if the data falls under the 1:1 line, it will indicate the negative trend 56 , 58 . If the scatter plot is more close to the 1: 1 path, the trend alter slope of that time period series will be weaker, along with the farther far from the 1:1 tier it is, the particular stronger the excitement change incline in the time series 55 . Often the straight-line trend slope displayed by the ITA method can be expressed as by the Eq.  13 56 :

Where s is the indicator of trend having the positive values that represent the increasing trend, while the negative values represent the decreasing trend. The \(\bar{{\rm{y}}}\) and \(\bar{{\rm{x}}}\) are the arithmetic average of the first sub-series (xi) and second sub-series (yi) respectively. Furthermore, n is represented by the number of collected data products. The indicator is then multiplied by 10 for comparing with MK test 61 .

Method for analyzing rainfall changes

To calculate atmospheric oscillations on rainfall trend variation, winter and summer precipitations and moisture divergence during 1979–2015 on 1.25° × 1.25° grids were obtained from the European Centre for Medium Range Weather Forecasts (ECMWF), ERA-5 ( http://apps.ecmwf.int/ datasets/data/interim-full-daily). The ERA5-Interim is the most recent ocean-atmospheric changes reanalysis datasets available since 1979 forwards. In addition, low cloud cover dataset was also derived from the ECMWF ERA5 data to assess the effect of cloud cover on rainfall variation 89 . We have quantified the influence of atmospheric circulation changes on the trend patterns in rainfall. At first, we have detected a recent significant change point of annual mean rainfall based on Pettit test for the period 1979–2015 in India and observed that the mean rainfall has a change point after 2000. Then, the change in circulation of the two periods before and afterword the changes are quantified by subtracting 1979–2000 from 2001 to 2015using the ECMWF ERA5 reanalysis data. The GrAdS software ( http://cola.gmu.edu/grads/ ) was used to prepare the spatial maps.

Method for future forecasting

Several probabilistic and deterministic methods like ARMA, ARIMA, SARIMA are usually employed to predict the hydrological and climatic datasets. These techniques have many drawbacks like serial correlation, non-linearity, and biasness to predict the non-linear hydro-climatic data sets. Therefore, the newly developed artificial intelligence (AI) models can able to overcome this drawback. Hence, the application of AI models is now widely popular to solve the environmental problems. However, in the present study, we employed artificial neural network, a popular AI model, to predict and forecast the rainfall of thirty-four meteorological sub-divisions. The ANN is a non-linear black-box AI model. This model works like parallel-distributed information processing system which reflecting biological structure of brain as it comprised of simple neurons and links that process information to establish an association between the inputs and outputs. Likewise the function of brain, the ANN model is working using feed-forward multilayer perceptron algorithm. This structure is consisting of three layer, such as an input layer, one or more hidden layer and an output layer. We selected the number of neurons in the hidden layer by trial and error procedure. The ANN learning is based on the structure and functions of biological neural networks i.e. enclosing adjustment to the synaptic links that present in the core of the neurons. The multi-layer perception (MLP) is one of the most extensively employed neural network typologies. The MLP with two hidden layers is the most used classifier for the stationary pattern classification. MLPs are generally functioned using the backpropagation algorithm. The back-propagation principle flourishes the errors from the network and permits adjustment of the unseen units. Two crucial characteristics of multilayer perceptron are:

(1) The non-linear processing elements (PEs) having non-linearity that should be smooth, so that the logistic functions and hyperbolic tangent can be frequently employed; and

(2) Their enormous interconnectivity (any constituent of a given layer feeds all the constituents of the next layer).

In the present study, we divided the whole rainfall datasets into training and testing datasets. Then we normalized the data and applied ANN model for predicting the annual rainfall. We applied ANN model again and again on the same rainfall datasets by changing the model’s parameters like seed, momentum, learning rate until the best ANN model achieved for prediction rainfall. In addition, we evaluated the performance of ANN for predicting using Root Mean Square Error (RMSE) techniques. When we achieved the best ANN model, we fixed the model parameters and applied on the rainfall data to forecast rainfall up to 2030. However, the rainfall prediction and forecasting were performed in Weka software (version 3.9) ( https://weka.informer.com/3.9/ ) and the mappings were done in Arc GIS (version 10.3) software.

Method for change rate calculation in rainfall time series data

The simple statistical method, percentage change is applied to calculate the change rate of the annual and seasonal rainfall for pre change and post change point. This method is very simple but the function of this method is much effective. It is calculated using Eq.  14 .

Spatial mapping using Kriging interpolation method

In this study, the ordinary kriging method was employed to interpolate the statistical data and prepare the spatial rainfall map. The ordinary kriging method works on the data having statistical properties or spatial autocorrelation and this method employed the semi-variogram model to represent the spatial linkage (autocorrelation). The semi-variogram model evaluates the power of spatial association as a function of distance between the data. Kriging is used to estimate the values Z*( x 0 ) at the point x 0 expressed 90 in Eq.  15 and the estimation of error variance \({\sigma }_{k}^{2}({x}_{0})\) , expressed in Eq.  16 .

Where λ i refers to weights; μ refers to LaGrange constant; and ( x 0  −  x i ) represents the semi-variogram value equivalent to the distance between x 0 and x i 91 , 92 . Nielsen and Wendroth 93 suggested that a semi-variogram is comprised of the regionalized variable theory and intrinsic hypotheses and it is expressed as follows:

Where, γ ( h ) represents semi-variance, h represent lag distance, Z refers to the rainfall-related parameters, N(h) is the number of couples of locations divided by the lag distance h, \(Z({x}_{i})\) , and \(Z({x}_{i}+h)\) refers to the values of Z at the positions x i and x i  +  h 94 . The empirical model of semi-variogram generated from collected data which was fitted to the theoretical semi-variogram. It was utilized to create geo-statistical properties which further include nugget structured and the sill variance as well as the distance parameter. In addition, the nugget-sill ratio has been calculated in order to characterize the spatial dependency of the statistical values. A nugget-sill ratio of 75% reflects a weak spatial dependency; or else the dependency is considered moderate.

Furthermore, all mappings in the present work were performed using kriging interpolation method in Arc GIS (version 10.3) software.

Results and Discussion

Descriptive analysis of annual rainfall.

We calculated the descriptive statistics of the annual rainfall since 1901 to 2015 for thirty-four meteorological sub-divisions of India. Results show that the South Indian meteorological divisions i.e. Kerala, Tamil Nadu, and Konkan & Goa have observed the highest average rainfall (3396.64 mm. 2930 mm. and 2974 mm. respectively). While, the minimum average rainfall has been recorded in the sub-divisions of West India meteorological divisions i.e. West Rajasthan (288.74 mm.), Saurashtra and Kutch (494.27 mm.), Haryana (535.47 mm.), Delhi and Chandigarh (596.16 mm.). The standard deviation of rainfall for whole India varies from 1242.04 to 108.99 mm. The highest variation (standard deviation) in rainfall was observed in Arunachal Pradesh meteorological sub-division, followed by Coastal Karnataka (480.98 mm.), Konkan & Goa (478. 49 mm.), while the minimum variation was recorded in Western Rajasthan (108. 99 mm., followed by North Interior Karnataka (135.33 mm), Haryana Delhi and Chandigarh (142. 64 mm). The skewness of the rainfall ranges from −0.81 to 1.05 for all sub-divisions of India. The negative skewness was found in Tamil Nadu (−0.81), Bihar (−0.39), Konkan & Goa (−0.21), Jharkhand (−0.15), West Uttar Pradesh (−0.09), East Madhya Pradesh (−0.02) and Madhya Maharashtra (−0.001). While rest of the meteorological sub-divisions were observed positive skewness.

The arithmetic mean is not significant (robust) to local variations 95 . Therefore, LOWESS regression curve was applied on the seasonal rainfall to minimize the local variation. A cluster of researchers used this statistical method and achieved satisfactory findings over the arithmetic mean 96 , 97 . The findings of LOWESS curve indicate an increasing pattern of annual rainfall upto1965, while a decreasing pattern of rainfall was found after the year 1970. (Fig.  1b ). The findings of LOWESS curve for the winter season (Fig.  1c ) showed that the increasing rainfall pattern was observed for the periods of 1935–1955 and 1980–1998. A sudden decrease in the trend was observed after 1955 and 1998. In the case of summer and monsoon season (Fig.  1d,e ), the curve indicates that the negative trend was observed after the 1960. The identical result was found in the work of 98 . Whilst, in the case of post-monsoon (Fig.  1f ), the LOWESS regression curve indicates the positive trend was found during 1925–1935 and 1955–1965. The negative trend for post monsoon was observed after 1995.

Long Term Pattern and variation of average annual and seasonal rainfall

We used the coefficient of variation techniques to explore the rainfall variation for all meteorological sub-divisions. The Fig.  2a–e showed the spatial mapping of variations of average annual and seasonal rainfall over India using ordinary kriging interpolation method which is geo-statistical approach. The findings of spatial mapping of rainfall variation showed that the meteorological sub-divisions of Western India were recorded highest rainfall fluctuations. The minimum rainfall fluctuation was registered in Assam and Meghalaya (11.35%) metrological divisions, followed by Sub-Himalayan meteorological divisions like West Bengal (12.25%), Orissa (12.92%) and extreme South Indian divisions like Kerala (14.16%), Coastal Karnataka (14.42%) and North Interior Karnataka (14.67%). This result indicates that these states have been experiencing very less inconsistent rainfall trend for 115 years. While, the highest rainfall variation was found in Saurashtra and Kutch (41.14%), followed by Western Rajasthan (37.75%), Arunachal Pradesh (33.23%) and Gujarat region (30.46%) indicating the irregular occurrences of rainfall throughout the year.

Meteorological subdivision wise spatial variations using the coefficient of variation (CV) in annual and seasonal rainfall where figure ( a ) shows annual, ( b ) winter, ( c ) summer, ( d ) monsoon, and ( e ) post monsoon rainfall pattern.

The analysis of distribution and fluctuation of rainfall over Indian meteorological divisions’ show that the occurrence of winter season rainfall was comparatively less than the other seasons during 1901–2015. The maximum variation of rainfall was reported in Konkan & Goa (253.37%), followed by Coastal Karnataka (187.70%), Gujarat region (185.12%), Saurashtra & Kutch (174.88%) and Madhya Maharashtra (162.06%) indicating the unstable incidence of rainfall in these meteorological units. More consistent rainfall with low variations was observed in Himachal Pradesh (41.89%), followed by Arunachal Pradesh (43.15%), Uttarakhand (48.69%) and Assam & Meghalaya (52.21%) meteorological units (Fig.  2c ). Result show that, the rainfall in summer season had not recorded a significant variation, whilst the maximum variations of rainfall was observed in the meteorological divisions of Western India. The lowest rainfall variation was found in the meteorological divisions of Northeastern India and extreme South India.

The study of diverge distribution of rainfall over India for monsoon season indicates that the maximum rainfall was registered in Saurashtra & Kutch (42.67% and 157.64%), followed by West Rajasthan (39.93% and 145.35%), Arunachal Pradesh. Whereas, the highest rain fall variation in post-monsoon season was found in Punjab (36.18% and 138.65%), followed by Gujarat region (31.38% and 127.66%) indicating unstable rainfall incidences (Fig.  2d,e ). The lowest rainfall fluctuation of monsoon season was recorded in the meteorological divisions of Assam & Meghalaya (12.69%), followed by Orissa (13.34%), Sub Himalayan West Bengal (13.94%) and Coastal Karnataka (15.29%). While, in the post-monsoon season, the lowest rainfall fluctuation was observed in Kerala (26.21%), followed by Tamil Nadu (29.46%) and South Interior Karnataka (36.44%).

Meteorological sub-division wise trends of Annual and Seasonal Rainfall

We calculated the annual and seasonal rainfall trend for thirty-four meteorological sub-divisions using non-parametric Mann-Kendall test (Table  1 ). The Table  1 shows the five shades of brown color based on the intensity of z value of Mann-Kendall test at 0.05 significance level which indicates that darker the shade of brown color, higher the negative z value and vice-versa. Results show that five sub divisions for annual rainfall were found in very dark shade zones (Nagaland Mizoram Manipur & Tripura, East Madhya Pradesh, Jharkhand, East Uttar Pradesh, Chhattisgarh and Kerala) which suggests that these sub-divisions were experienced highly negative trend of rainfall (more than −2 of z value). The z value of MK test ranges from 0 to −2 which could be found in eight meteorological sub-divisions, i.e. Bihar, Orissa, Assam & Meghalaya, West Uttar Pradesh, Uttarakhand, Himachal Pradesh, Vidarbha and East Rajasthan (dark brown color in Table  1 ). The z value having the positive trend varies from 0 to 2 which were observed in the eleven meteorological sub-divisions that implies the increasing nature of rainfall over these divisions, while the six meteorological sub-divisions recorded highly positive trend of more than 2 of z value (very light shade of brown color in Table  1 ), which indicates the significant increase of rainfall over time in these meteorological units.

The two meteorological division (Madhya Maharashtra and North Interior Karnataka) for summer season and seven meteorological divisions (Assam & Meghalaya, Orissa, Bihar, Punjab, East Rajasthan, Chhattisgarh and Kerala) for monsoon season were recorded highly negative trend in rainfall having >2 of z value (very dark shade rows in Table  1 ). On the other hand, no meteorological divisions for winter and post-monsoon season were recorded as highly negative trend (more than >2 of z value) in rainfall, while most of the sub-divisions were detected as negative trend having the z value 0 f 0 to −2 (dark shade rows in Table  1 ).

The Change Point Detection analysis for annual and seasonal rainfall

The above-mentioned analysis (Table  1 ) explained that few meteorological sub-divisions were detected as significant negative trend having the z value of >−2. However, several researchers claimed that the actual trend could not be detected if we apply the MK test on overall hydro-climatic datasets. Therefore, they highly recommended to apply the change detection techniques before the application of MK test. Hence, we utilized change detection methods like Pettitt test, SNHT test and Buishand range test for detecting the abrupt change point in the rainfall datasets in thirty-four meteorological sub-divisions (Supplementary Table  1 ). The Supplementary Table  1 shows that the annual and seasonal rainfall of all meteorological sub-divisions had the abrupt change point which were detected by mentioned three change detection techniques. Furthermore, we selected the change point for annual and seasonal rainfall in each sub-divisions based on the performances (p value) of these tests (Supplementary Table  1 ). Therefore, these abrupt change points suggest that the rainfall datasets had no monotonous trend. The selected change point for seven meteorological sub-divisions (East Uttar Pradesh, West Uttar Pradesh, Punjab, and Gujarat region, Saurastra & Kutch, Coastal Andhra Pradesh and Tamil Nadu) were after 1990s. The nineteen meteorological sub-divisions had the abrupt change point during the period of 1950–1980. The abrupt change point year for the rest of the meteorological divisions were detected before 1940.

Change point wise annual and seasonal variation analysis

We computed the annual and seasonal rainfall variation in pre and post change point wise for all meteorological sub-divisions to explore the dynamics of the intensity of annual and seasonal rainfall variations after the change point. Therefore, we can consider this change point wise rainfall variation analysis as a validating method for the relevancy of uses of the change point methods and further research. Hence, to prove this statement, we computed and prepare the spatial map of annual and seasonal rainfall variation for pre change point (Fig.  3a–e ) and post change point (Fig.  3f–j ) using coefficient of variation method. Results show that highest fluctuation (25% to 239.02%) in annual and seasonal rainfall was observed in the sub-divisions of Western India and North-Western India (3a-e). Whereas, the minimum fluctuation in annual and seasonal rainfall having the CV of 0.36% to 7.76% was observed in the sub-divisions of North Eastern India, Eastern India and Northern India indicating the consistent rainfall occurrence in these region.

Spatial variation of rainfall measured using the coefficient of variation for pre-change point where figure ( a ) shows annual rainfall, ( b ) winter, ( c ) summer, ( d ) monsoon, and ( e ) post monsoon; post-change point where figure ( f ) shows annual rainfall, ( g ) winter ( h ) summer ( i ) monsoon and ( j ) post monsoon; spatial changes in the rate of rainfall where figure ( k ) shows annual rainfall, ( l ) winter ( m ) summer ( n ) monsoon and ( o ) post monsoon.

The findings of annual and seasonal rainfall analysis in post change point phase show that the large areas of the country like the sub-divisions of Western India, Central India, and South Western India observed high fluctuation having the CV of 22% to 209.82%. In case of monsoon and post monsoon season, the sub-divisions of North Western were experienced by high variation of rainfall suggesting the high tendency of inconsistently rainfall occurrences in these regions. While the lowest rainfall variation having the CV of 0.75% to 11.64% was recorded in sub-divisions of North-Eastern India, North India, East India of winter and summer rainfall and Central India of Post monsoon rainfall. The intensity of minimum rainfall variation range was increased in post change point phase which suggests that inconsistency rainfall events were observed. However, in the post change point phase, the area coverage of lower variation of rainfall incidences were reduced significantly that implies the climate change and validation of the application of change point detection methods.

Rainfall change rate analysis

In the present study, we computed the rainfall change rate for annual and seasonal rainfall in all meteorological sub-divisions based on the calculation between the rainfall data of pre and post change point phase. The Fig.  3k–o shows the spatial mapping of rainfall change rate for annual and seasonal rainfall. Results show that sub-divisions of Western India were observed highest change rate (47% to 58.54%) in annual rainfall, while the sub-divisions of whole country except West India were registered the highest change rate (−0.5% to −1.77%) in winter rainfall. In case of summer and monsoonal rainfall, the sub-divisions of western were observed highest negative change rate (34.22% to 67.04), while the South India and North East India were recorded highest negative change rate in post monsoon rainfall. Furthermore, the North India and Central India were recorded minimum change rate, whereas, the Western India of winter and post-monsoon rainfall. This analysis signifies that the amount of rainfall occurrences was decreased significantly after change point.

Change point wise seasonal rainfall trend analysis

We applied MK test on the datasets of pre and post change point of seasonal rainfall in each of the meteorological sub-divisions as per the recommendation of many scientists. The Table  2 reported the seasonal rainfall trend for pre change point in all sub-divisions Results show that eight meteorological sub-divisions in monsoon, six sub-divisions in post monsoon, fourteen sub-divisions in both summer and winter season were recorded negative trend having the z value of 0 to −2 (dark shade of brown color rows in Table  2 ). While, three sub-divisions in summer and four sub-divisions in winter seasons were recorded highly negative trend having z value of > −2 (very dark shade of brown color rows in Table  2 ). Rests of the meteorological sub-divisions were detected as positive trend (lighter shade of brown color in Table  2 ) except one division (Sub Himalayan West Bengal & Sikkim) which detected has no trend. From the analysis, it can be stated that non-monsoonal seasons were recorded as declining trend.

The Table  3 showed the trend analysis using MK test for post-change point seasonal rainfall. Seventeen meteorological sub-divisions in monsoon, sixteen sub-divisions in post monsoon, seventeen sub-divisions in summer and twenty one sub-divisions in winter seasons were observed the negative trends having the z value of 0 to −2 indicating that huge amount area was recorded declining rainfall than the pre-change point phase (dark shade of brown color rows in Table  3 ). The increasing of meteorological divisions which have the negative trend from the pre change point (Table  3 ). This circumstance implies that the declining trend of rainfall was increased manifold after post change point which shows the sign of climate change. On the other hand, rest of the meteorological sub-divisions were experienced the insignificant positive trend having the z value of <0.5.

Innovative trend analysis for seasonal rainfall

Several researchers suggested that non-parametric statistical techniques like mann-kendall test has many drawbacks like the presence of serial correlation within the data sets, non-linearity and most importantly sample size which could have ability to influence the result. Therefore, Sen 55 developed innovative trend method which can overcome the mentioned drawbacks, especially the problem sample size. Sen 55 reported that innovative trend can effectively able to detect the trend on any numbers of sample size and presence of serial correlation. Hence, we used innovative trend to calculate the trend for seasonal rainfall (winter, summer, monsoon and post monsoon) in thirty-four meteorological sub-divisions (Supplementary Figure  1 – 4 ). We computed D value of innovative trend to compare the intensity of trend achieved by MK test. The spatial mapping using D values of innovative trend for seasonal rainfall was presented in Fig.  4a–d . However, the Supplementary Table  2 showed the slope value of innovative trend for seasonal rainfall in all meteorological sub-divisions. The findings indicate that the negative trend was detected in the sub-divisions of North Eastern, Central and Southern India for summer season. The results were quite identical with the findings of MK test, but the magnitudes of the trend were different which stated that the region was experienced strong negative trend (Fig.  4 ). Although the highly positive trend was detected in the sub-divisions of Rajasthan part and Jammu-Kashmir region which does not imply that the rainfall occurrences was increased, but the regions were received more or less consistent amount of rainfall throughout the time periods, while little amount of rainfall was increased in few years over these regions which is the reason for positive trend. However, in case of monsoon rainfall, the negative slope of innovative trend was detected in the sub-divisions of North Eastern part, Eastern part and some parts of the central India, whereas, rest of the sub-divisions were detected as insignificant positive slope of trend. The sub-divisions of North Eastern states, Bihar, Orissa, Jharkhand, Western Ghat and Punjab regions were experienced very strong negative slope of innovative trend, on the other hand, rest of the part were recorded insignificant positive slope of trend for post monsoon rainfall. In case of winter rainfall, the meteorological sub-divisions of Central part of India, Southern India, Western Ghat regions were observed the negative trend, while the sub-divisions of North Eastern, Western and Eastern part of India were experienced positive slope of trend. Therefore, the findings of MK test and innovative trend analysis were highly identical, however, few states where no significant trend were detected using MK test, but in the case of innovative trend, those region were come under negative trend. However, the findings from both trend detected methods clearly stated that India has been experiencing fewer downpours than the expected rainfall since last 30 years that clearly points out about the climate change. Several researches established that innovative trend can able to detect trend effectively over the others non-parametric test 18 , 99 , 100 , . Therefore, we considered innovative trend as a tool of intensity of trend measurement over the other techniques and results show that the highly negative trend was detected in most of the sub-divisions indicate about the climate change.

The spatial variation of slope of innovative trend for ( a ) summer, ( b ) monsoon, ( c ) post monsoon and ( d ) winter.

Micro level rainfall change rate analysis

In the present study, we attempted to analyze the change rate of annual rainfall for each and every year in thirty-four meteorological sub-divisions. We computed the change rate by calculating the departure of year wise average rainfall from the long-term average rainfall. The heat map was used to show the dynamics of year wise rainfall change rate for all sub-divisions (Fig.  5 ). This heat map was generated from R software (version 3.5.3) ( https://cran.r-project.org/bin/windows/base/old/3.5.3/ ) using the ggplot2 package 101 ( https://cran.r-project.org/web/packages/ggplot2/index.html ). The intensity of change rate was represented by the shades of red and green color indicating the highly negative change rate and vice versa. Results show that the after 1970, all meteorological sub-divisions were observed the negative departure in almost all years from long-term average rainfall by 50 mm.−2000 mm. (Fig.  5 ). While the sub-divisions of North-Eastern India and North India were recorded positive change rate only for few years because of the occurrences of excessive rainfall that was happened occasionally by monsoon burst, ELSO effect (Kripalini et al . 2003) and local climatic effect. However, the highest negative change rate was observed in the sub-divisions like Arunachal Pradesh, Nagaland Manipur, Mizoram & Tripura, Kerala, Western Uttar Pradesh, Rajasthan, Uttarakhand and Himachal Pradesh by more than −2000 mm. While the positive change rate was mainly detected before change detection year or 1970 in all sub-divisions. The positive change rate was varied from 0–2000 mm. However, few sub-divisions like Kerala, Nagaland Manipur Mizoram & Tripura, and Coastal Karnataka.

Heatmap represents the departure of rainfall from normal rainfall for all meteorological subdivision. (N.B. CK- Coastal Karnataka, K & G – Konkan and Goa, HP- Himachal Pradesh, UK- Uttarakhand, EMP- Eastern Madhya Pradesh, CG- Chhattisgarh, JK – Jharkhand, J & K – Jammu and Kashmir, S & K- Saurashtra and Kachcha, GR – Gujarat region, GWB – Gangetic West Bengal, NIK – North Interior Karnataka, SIK – South Interior Karnataka, TN – Tamilnadu, CAP – Coastal Andhra Pradesh, WUP – western Uttarpradesh, EUP – Eastern Uttarpradesh, ER – Eastern Rajasthan, WR – Western Rajasthan, HD & C- Haryana Delhi and Chandigarh, WMP – WesternMadhyapradesh, MM – MadhyaMadhyapradesh, A & M- Assam and Meghalaya, SHW – Sub Himalaya West Bengal, NMM & T- Nagaland, Manipur, Mizoram and Tripura, AP- Arunachal Pradesh.

Rainfall prediction and forecasting

The above mentioned analyses show that most of the meteorological sub-divisions of India were experienced significant decrease in rainfall and this decrease was become stronger after the change point. Therefore, the situation of rainfall incidence became critical post change point. Therefore, if the present situation of rainfall occurrences continues with the same intensity, the intensity of rainfall occurrences and their distribution in future will be more worsen. Therefore, the prediction and forecasting of rainfall is become essential for water resource management and planning. Several soft computing machine learning techniques are available for predicting and forecasting rainfall, but MLP based artificial neural network has become widely popular among the researchers as it is easy to compute and generate high quality product 102 , 103 . In this study, we applied MLP-ANN on the annual rainfall data for prediction. We set the parameters of artificial neural network models again and again until the best prediction had not achieved which we evaluated using RMSE techniques. We fixed the model’s parameters and applied on the rainfall of thirty-four meteorological sub-divisions for prediction. In some cases, we changed some of models parameters for predicting in rainfall. However, the Supplementary Table  3 shows the plot between the best MLP-ANN generated model and observed rainfall for thirty four meteorological sub-divisions that indicates that there is close adjacency between predicted and observed rainfall. The error measures like RMSE and MAE (in mm.) were used to quantify the closeness between predicted and observed rainfall. The Supplementary Table  4 reports the 15 years rainfall forecasting up to 2030 for all meteorological sub-divisions. We selected the rainfall of 2020 and 2030 as the representative for spatial mapping (Fig.  6 ) (for the rainfall forecasting data of all years was presented in Supplementary Table  4 ). The findings of spatial mapping of future forecasting show the more declining amount of rainfall.

Spatial rainfall forecast for 2020 ( a ) and 2030 ( b ).

In 2020, the maximum rainfall incidence will be observed in the sub-divisions of Coastal Karnataka, Konkan & Goa, Kerala (>2500 mm rainfall) and the minimum rainfall event will be placed on the West Rajasthan, Tamil Nadu, East Rajasthan, Rayalseema (<400 mm rainfall) (Fig.  6a ). On the other hand, in 2030, the highest rainfall will be occurred in the sub-divisions of Coastal Karnataka, Konkan & Goa, Kerala (>2000 mm rainfall), while the lowest rainfall event will be found in the sub-divisions like West Rajasthan, East Rajasthan, Tamilnadu, Rayalseema (<300 mm rainfall) (Fig.  6b ). The findings of future forecasting analysis point out that the occurrences of rainfall will be decreasing gradually for some meteorological stations, while the rainfall will be increased in some meteorological sub-division. The places of rainfall occurrences over India will be remained as like identical pattern of present and earlier rainfall incidences. The findings also show that the extreme rainfall events will be increased in future that will be lead to flooding situation.

Causes of rainfall variation

India encompasses large areas with moderate to high convective precipitation, while low convective rainfall rate occurred which was brought excessive moisture from the Indian Ocean to the in land areas, which is unfavorable for the formation of rainfall (Fig.  7a–f ). This caused a decreased in mean rainfall during monsoon season, which has been distributed in the extreme southern and mid-central divisions of the country (Fig.  7 ). The northeasterly wind was strengthened in the whole country, which reduced the invasions of cooler air, led to decline in rainfall all over India in the winter season. By contrast, most of the divisions had moderate to high mean convective precipitation rate, which increased rainfall to some extent. The low cloud has increased all over the India, and a few cloud covers will enhance the consolidation impact of atmosphere on the solar radiation, and hence it leads to declining rainfall trend. Moreover, high mean total precipitation rate has been influencing across the country except for eastern division, which triggered uneven downdraft pattern and leading to more clear sky days during the recent study period (1979–2017). Most of the regions in India exhibited a declining vertically integrated moisture divergence which triggered by a significant decreasing rainfall changes.

Spatial variations of differences in ( a ) convective precipitation in monsoon season, ( b ) convective rainfall rate in winter season, ( c ) low cloud cover, ( d ) mean convective precipitation rate, ( e ) mean total precipitation rate, and ( f ) mean vertically integrated moisture divergence between the recent period of 2001–2015 and 1979–2000.

Marumbwa et al . 104 suggested that the analysis of historical rainfall trend is very crucial in several fields like water resource management, sustainable agricultural planning, ecosystem management and health sector. The rainfall data of 115 years were used to investigate the variability of annual and seasonal rainfall in very detail and analyzed the trend in several ways for thirty-four meteorological sub-divisions. The findings of 115 years annual and seasonal rainfall variation analysis show that the highest rainfall variation was found in the sub-divisions of Western India of annual and summer, Western India and South Western India of winter, North India and Western India of monsoon and Western, North Western and some parts of Eastern India of post monsoon rainfall. While the lowest variation was found in the sub-divisions of North Eastern and Eastern India. The identical result was reported by several previous studies 32 , 45 , 66 , 71 , 105 . The findings of MK test on annual and seasonal rainfall report that thirteen meteorological sub-divisions were observed negative trend, while rest of the sub-divisions were recorded positive trend. Rajeevan et al . 106 and Guhathakurta and Rajeevan 46 reported that Jharkhand, Chhattisgarh and Kerala were observed negative trend, while eight sub-divisions like Gangetic West Bengal, Coastal Andhra Pradesh, Kanakan & Goa, North Interior Karnataka, Rayalessema, Jammu &Kashmir were recorded decreasing trend over time. These results are totally identical with the findings of the MK test of the present study (Very dark shade of brown color rows in Table  1 ). Mirza et al . 107 reported that the annual and seasonal rainfall was more or less consistent in sub-divisions sub-himalayan Bengal and Gangetic Bengal which was by and large similar with the findings of MK test in the present study (Table  1 ).

Mondal et al . 32 used Pettitt test and SNHT test for detecting change point in the annual rainfall for all meteorological sub-divisions for their study and reported that twenty-one sub-divisions had the change point using Pettitt test between 1950–1966. While we detected eighteen sub-divisions that have change point between 1950–1966 (Supplementary Table  1 ). Goyal 98 documented that the most probable change point was 1959, while in the present study, most of the change points were detected in between 1950–1966 which can conclude that the findings of present work are identical with the work of Goyal. Therefore this study concludes that 115 years annual rainfall had not the monotonous trend and it needs for further research to detect the trend and its magnitude accurately. Based on which planners and scientists can propose the developmental and management plan.

Furthermore, MK test was employed on the annual and seasonal rainfall data of both pre and post change point phase to detect the exact and accurate trend as several scientists recommended to apply MK test based on the analysis of change point to achieve the accurate trend 34 , 38 , 108 . The misleading results could be found if the MK test applies first 38 . However, The findings of MK test on the annual and seasonal rainfall for pre change point phase stated that among the 34 meteorological divisions, 8 sub-divisions of each monsoon and post monsoon, 17 sub-divisions of summer and 18 sub-divisions of winter season were detected insignificant negative but trend (Table  2 ). Parthasarathy and Dhar 109 documented that most of the sub-divisions were recorded positive trend for the period 1901–1970 that was the change point year for the most of the sub-divisions in the present study (Table  2 and Supplementary Table  1 ).

The findings of the MK test for post change point phase showed that the negative significant trend was detected in the 17 meteorological sub-divisions of monsoon, 15 sub-divisions of post monsoon, 19 sub-divisions of summer and 21 sub-divisions of winter season (Table  3 ). Jain and Kumar 110 reported that 4 river basins of India were detected as increasing trend, while 13 river basins were observed significant negative trend in monsoon rainfall. In case of annual rainfall, 15 river basins were recorded negative trend. Therefore, we can state that these findings are supporting the present work. Guhathakurta et al . 111 reported that significant negative trend was found in NW and Central and Peninsular India for the period of 1951–2011. Several studies also reported that the significant amount of rainfall was decreased over time, especially after 1950 44 , 46 , 81 , 105 , 112 .

To the best of authors’ knowledge, the application of recently developed innovative trend analysis to detect rainfall trend was very rare. We found the work of Machiwal et al . 113 who applied innovative trend analysis to detect trend in rainfall and temperature of Indian arid region. Machiwal et al . 113 documented that increasing trend for monsoon rainfall was found in the arid region and this result is identical with the findings of the present study. However, the findings of Innovative trend analysis on annual and seasonal rainfall showed that negative trend was observed in the sub-divisions of NE, E, SE and S part of summer, NE and some parts of E India of monsoon, NE, E India of post-monsoon and W, SW, S India of winter rainfall. The identical finding was found in the work of Mondal et al . 32 . Taxak et al . 96 studied grid wise rainfall trend over India and documented that most of grids were received negative trend except seven grids which were observed positive trend. The findings of this study are totally identical with the present work.

Dash et al . 53 and Kumar et al . 11 documented that frequency of intense rainfall in many parts of Asia has increased, while the amount of rainfall and number of rainy days has decreased significantly. For this reason, the year wise departure study revealed that some of the years were observed positive departure from the long-term average rainfall by more than 1000 mm rainfall (Fig.  5 ). Even the identical findings can be found in the work of Goswami et al . 40 and they reported that the frequency and magnitude of extreme rainfall has amplified significantly, while the frequency and intensity of moderate rainfall has observed noteworthy decreasing trend.

In the present study, the change rate was maximum in the meteorological sub-divisions of Western and Central India in annual and monsoon rainfall, whereas the maximum change rate (%) for winter season was in the sub-divisions of Central, North East and Eastern India. Mondal et al . 32 reported that many parts of Western India and Central India were received very high negative change rate for monsoon rainfall, while Central and North Eastern parts of India were observed highly decreasing change rate. Furthermore, it can be concluding from the analysis that North Eastern part of India has been observed the worst effect of climate change than any other parts of India.

In the present study, the annual rainfall for thirty-four meteorological sub-divisions were predicted and forecasted up to 2030. In addition, the rainfall forecasting for 2030 showed an expected decline of about 5–10% in the overall rainfall of India (Fig.  6 ). However, numbers of studies were conducted to predict and forecast rainfall for India based on the average rainfall and they did not consider studying for all parts of the India. Guhathakurta 114 predicted the rainfall using neural network for Kerala. While Chakraborty et al . 49 predicted the south-west monsoon for India. They used average time series annual rainfall dataset of India. But in the present study, we considered rainfall datasets for thirty-four meteorological sub-divisions. Therefore, the findings of this study would not be generalized; rather it would be more accurate and can be act as the foundation of the developmental planning.

The temporary change in rainfall distribution significantly distresses the agricultural production. It will increase the drought protection and resilience plans under the changing climate conditions. Intergovernmental Panel on Climate change (IPCC) reported that upcoming changes in climate is to be likely to distress agriculture that will amplify the chance of hunger and water paucity, as well as the instructions on quicker thawing of glaciers 115 . Gosain et al . 116 pointed that the amount of freshwater in the river of India is probable to be diminish because of changing climate. This reduction, along with growing population might unfavourably affect a large population in India by 2050.

Large-scale ocean-atmospheric changes derived from the ECMWF ERA5 re-analysis data depicted that compared between 1979–2000 to 2001–2015 period, an increasing/decreasing convective precipitation rate, enhanced low cloud cover and inadequate moisture variance in the Indian ocean being transported to the northwest direction might have highly influenced the rainfall trend in India.Therefore, it is expected that this study will provide an insight for the management and development of agriculture and water resources in India to overcome the possible impacts of the climate change.

The basic and essential requirement for the management and planning of water resource, sustainable agricultural development and other sectors is the exploration of the spatiotemporal distribution and changing pattern of rainfall in any places. Hence, the present study investigated the variability and trend analysis of annual rainfall in several ways like overall data, change point wise (pre and post change point) using 115 years long-term annual and seasonal rainfall data of thirty-four meteorological sub-divisions. The present study shows that the overall annual and seasonal variability of rainfall was highest in the sub-divisions of Western India, while the lowest variability was found in Eastern and North India. The findings of MK test on overall annual and seasonal rainfall reports that the sub-divisions of North-East, South and Eastern India were detected significant negative trend, while the sub-divisions like Sub-Himalayan Bengal, Gangetic Bengal, Jammu & Kashmir, Konkan & Goa, Madhya Maharastra and Marathwada were recorded positive trend. Furthermore, the change detection techniques were utilized and selected the change point based on the performances of the techniques. The most probable change points were detected in between 1950–1966. Based on the change point year, the rainfall variability and trend analysis were again carried out for pre and post change point phase. The rainfall variability was increased significantly in most of the meteorological sub-divisions after post change point and similar kinds of findings were found when the rainfall trend was analyzed for post change point. To get better results of trend analysis, the innovative trend analysis was employed. The finding shows that most of the sub-divisions were recorded significant negative trend. Even some of the sub-divisions were detected as no trend using MK test, but the trend was detected using innovative trend analysis. However, the micro level change rate analysis was used in the present study and the results show that after 1960, most of the meteorological sub-divisions were recorded more than −500mm rainfall departure from the long-term average rainfall in many years, while few years were detected positive departure in few sub-divisions. Therefore, from the detail analysis, it is established that almost all of the sub-divisions were detected the negative trend and high variability after 1970. Even the year wise study also revealed that in which year and how much rainfall amount was departed. Hence, these detail information regarding historical data for whole country are very beneficial for the planning. One of the most striking features of developmental planning in the recent time is the forecast of the incoming event which can be found in any sector like finance, water resource and most importantly climatology. Therefore, in the present study, the rainfalls for all meteorological sub-divisions were forecasted using the advance AI models like artificial neural network. The findings of the rainfall forecasting show that 15% of rainfall will be declined in 2030 that indicates the alarming situations will be appeared for both environment and living world.

The economy of India is totally dependent on the rainfall either it is agriculture or industry. Therefore, the water resource is essential part of the progressive economy of India. But due to climate change, the world rainfall pattern has been disturbed. Therefore, many studies were carried out in the developed countries to quantify the pattern of the rainfall changes and formulate the management plan accordingly. But in case of India, very fewer studies were done to do so. The present study provides information in all aspects like rainfall variability and trend for overall and change point wise for annual and seasonal rainfall, rainfall change rate since change point year, year wise departure, and future rainfall and most importantly this study analyzes the causes of rainfall changes in India. Technically, the present study used several sophisticated techniques which have been admired worldwide by the scientists for providing high precision results. This type of studies has not been conducted for whole India. Therefore, the present study can be the full package and should be very much helpful to the Indian planners to proposing plans for small and large scale regions.

To formulate the management plan for the sustainable development of water resource based sectors and environment, the scientist of others countries can conduct the research like the present study as they need lots of information for developing plan regarding historical, present and future data which can be in any field like hydrology, climatology.

However, in the present study, we considered thirty-four meteorological sub-divisions for the research, but to be more accurate, micro level data like district wise data should be incorporated. Then the very high precision micro level management plan will be achieved. Even, the grid wise rainfall study using very advanced microwave remote sensing technology will be very useful for the planners. The ensemble machine learning techniques, deep learning techniques like long-short-term memory (LSTM) network can be used to achieve very high quality forecasting data.

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Acknowledgements

The authors of the article would like to thank Indian Meteorological Department for providing the rainfall data of all sub-divisions to conduct this work. The authors also acknowledge the Department of Geography, University of Gour Banga, Malda, West Bengal and Department of Geography and Jamia Milia Islamia, New Delhi for providing laboratory facilities and other supports to carry out the research.

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S.T. participated in the design of this study, and performed the statistical analysis, modeling and revise the manuscript. J.M. carried out data acquisition, data processing and some statistical analysis. A.R.M modeled cause of rainfall changes. Shahfahad wrote the initial draft, B.P. collected background information. S.T., Shahfahad, and S.M. carried out literature review. S.T., S.M., A.R.M., A.R., and Shahfahad reviewed and revised the manuscript. P.S. reviewed the manuscript. All authors have read and approved the content of the manuscript.

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Praveen, B., Talukdar, S., Shahfahad et al. Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Sci Rep 10 , 10342 (2020). https://doi.org/10.1038/s41598-020-67228-7

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A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Kashif abbass.

1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 People’s Republic of China

Muhammad Zeeshan Qasim

2 Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094 People’s Republic of China

Huaming Song

Muntasir murshed.

3 School of Business and Economics, North South University, Dhaka, 1229 Bangladesh

4 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh

Haider Mahmood

5 Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, 173, Alkharj, 11942 Saudi Arabia

Ijaz Younis

Associated data.

Data sources and relevant links are provided in the paper to access data.

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Introduction

Worldwide observed and anticipated climatic changes for the twenty-first century and global warming are significant global changes that have been encountered during the past 65 years. Climate change (CC) is an inter-governmental complex challenge globally with its influence over various components of the ecological, environmental, socio-political, and socio-economic disciplines (Adger et al.  2005 ; Leal Filho et al.  2021 ; Feliciano et al.  2022 ). Climate change involves heightened temperatures across numerous worlds (Battisti and Naylor  2009 ; Schuurmans  2021 ; Weisheimer and Palmer  2005 ; Yadav et al.  2015 ). With the onset of the industrial revolution, the problem of earth climate was amplified manifold (Leppänen et al.  2014 ). It is reported that the immediate attention and due steps might increase the probability of overcoming its devastating impacts. It is not plausible to interpret the exact consequences of climate change (CC) on a sectoral basis (Izaguirre et al.  2021 ; Jurgilevich et al.  2017 ), which is evident by the emerging level of recognition plus the inclusion of climatic uncertainties at both local and national level of policymaking (Ayers et al.  2014 ).

Climate change is characterized based on the comprehensive long-haul temperature and precipitation trends and other components such as pressure and humidity level in the surrounding environment. Besides, the irregular weather patterns, retreating of global ice sheets, and the corresponding elevated sea level rise are among the most renowned international and domestic effects of climate change (Lipczynska-Kochany  2018 ; Michel et al.  2021 ; Murshed and Dao 2020 ). Before the industrial revolution, natural sources, including volcanoes, forest fires, and seismic activities, were regarded as the distinct sources of greenhouse gases (GHGs) such as CO 2 , CH 4 , N 2 O, and H 2 O into the atmosphere (Murshed et al. 2020 ; Hussain et al.  2020 ; Sovacool et al.  2021 ; Usman and Balsalobre-Lorente 2022 ; Murshed 2022 ). United Nations Framework Convention on Climate Change (UNFCCC) struck a major agreement to tackle climate change and accelerate and intensify the actions and investments required for a sustainable low-carbon future at Conference of the Parties (COP-21) in Paris on December 12, 2015. The Paris Agreement expands on the Convention by bringing all nations together for the first time in a single cause to undertake ambitious measures to prevent climate change and adapt to its impacts, with increased funding to assist developing countries in doing so. As so, it marks a turning point in the global climate fight. The core goal of the Paris Agreement is to improve the global response to the threat of climate change by keeping the global temperature rise this century well below 2 °C over pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5° C (Sharma et al. 2020 ; Sharif et al. 2020 ; Chien et al. 2021 .

Furthermore, the agreement aspires to strengthen nations’ ability to deal with the effects of climate change and align financing flows with low GHG emissions and climate-resilient paths (Shahbaz et al. 2019 ; Anwar et al. 2021 ; Usman et al. 2022a ). To achieve these lofty goals, adequate financial resources must be mobilized and provided, as well as a new technology framework and expanded capacity building, allowing developing countries and the most vulnerable countries to act under their respective national objectives. The agreement also establishes a more transparent action and support mechanism. All Parties are required by the Paris Agreement to do their best through “nationally determined contributions” (NDCs) and to strengthen these efforts in the coming years (Balsalobre-Lorente et al. 2020 ). It includes obligations that all Parties regularly report on their emissions and implementation activities. A global stock-take will be conducted every five years to review collective progress toward the agreement’s goal and inform the Parties’ future individual actions. The Paris Agreement became available for signature on April 22, 2016, Earth Day, at the United Nations Headquarters in New York. On November 4, 2016, it went into effect 30 days after the so-called double threshold was met (ratification by 55 nations accounting for at least 55% of world emissions). More countries have ratified and continue to ratify the agreement since then, bringing 125 Parties in early 2017. To fully operationalize the Paris Agreement, a work program was initiated in Paris to define mechanisms, processes, and recommendations on a wide range of concerns (Murshed et al. 2021 ). Since 2016, Parties have collaborated in subsidiary bodies (APA, SBSTA, and SBI) and numerous formed entities. The Conference of the Parties functioning as the meeting of the Parties to the Paris Agreement (CMA) convened for the first time in November 2016 in Marrakesh in conjunction with COP22 and made its first two resolutions. The work plan is scheduled to be finished by 2018. Some mitigation and adaptation strategies to reduce the emission in the prospective of Paris agreement are following firstly, a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels, secondly, to aim to limit the rise to 1.5 °C, since this would significantly reduce risks and the impacts of climate change, thirdly, on the need for global emissions to peak as soon as possible, recognizing that this will take longer for developing countries, lastly, to undertake rapid reductions after that under the best available science, to achieve a balance between emissions and removals in the second half of the century. On the other side, some adaptation strategies are; strengthening societies’ ability to deal with the effects of climate change and to continue & expand international assistance for developing nations’ adaptation.

However, anthropogenic activities are currently regarded as most accountable for CC (Murshed et al. 2022 ). Apart from the industrial revolution, other anthropogenic activities include excessive agricultural operations, which further involve the high use of fuel-based mechanization, burning of agricultural residues, burning fossil fuels, deforestation, national and domestic transportation sectors, etc. (Huang et al.  2016 ). Consequently, these anthropogenic activities lead to climatic catastrophes, damaging local and global infrastructure, human health, and total productivity. Energy consumption has mounted GHGs levels concerning warming temperatures as most of the energy production in developing countries comes from fossil fuels (Balsalobre-Lorente et al. 2022 ; Usman et al. 2022b ; Abbass et al. 2021a ; Ishikawa-Ishiwata and Furuya  2022 ).

This review aims to highlight the effects of climate change in a socio-scientific aspect by analyzing the existing literature on various sectorial pieces of evidence globally that influence the environment. Although this review provides a thorough examination of climate change and its severe affected sectors that pose a grave danger for global agriculture, biodiversity, health, economy, forestry, and tourism, and to purpose some practical prophylactic measures and mitigation strategies to be adapted as sound substitutes to survive from climate change (CC) impacts. The societal implications of irregular weather patterns and other effects of climate changes are discussed in detail. Some numerous sustainable mitigation measures and adaptation practices and techniques at the global level are discussed in this review with an in-depth focus on its economic, social, and environmental aspects. Methods of data collection section are included in the supplementary information.

Review methodology

Related study and its objectives.

Today, we live an ordinary life in the beautiful digital, globalized world where climate change has a decisive role. What happens in one country has a massive influence on geographically far apart countries, which points to the current crisis known as COVID-19 (Sarkar et al.  2021 ). The most dangerous disease like COVID-19 has affected the world’s climate changes and economic conditions (Abbass et al. 2022 ; Pirasteh-Anosheh et al.  2021 ). The purpose of the present study is to review the status of research on the subject, which is based on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures” by systematically reviewing past published and unpublished research work. Furthermore, the current study seeks to comment on research on the same topic and suggest future research on the same topic. Specifically, the present study aims: The first one is, organize publications to make them easy and quick to find. Secondly, to explore issues in this area, propose an outline of research for future work. The third aim of the study is to synthesize the previous literature on climate change, various sectors, and their mitigation measurement. Lastly , classify the articles according to the different methods and procedures that have been adopted.

Review methodology for reviewers

This review-based article followed systematic literature review techniques that have proved the literature review as a rigorous framework (Benita  2021 ; Tranfield et al.  2003 ). Moreover, we illustrate in Fig.  1 the search method that we have started for this research. First, finalized the research theme to search literature (Cooper et al.  2018 ). Second, used numerous research databases to search related articles and download from the database (Web of Science, Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and Sciverse). We focused on various articles, with research articles, feedback pieces, short notes, debates, and review articles published in scholarly journals. Reports used to search for multiple keywords such as “Climate Change,” “Mitigation and Adaptation,” “Department of Agriculture and Human Health,” “Department of Biodiversity and Forestry,” etc.; in summary, keyword list and full text have been made. Initially, the search for keywords yielded a large amount of literature.

Methodology search for finalized articles for investigations.

Source : constructed by authors

Since 2020, it has been impossible to review all the articles found; some restrictions have been set for the literature exhibition. The study searched 95 articles on a different database mentioned above based on the nature of the study. It excluded 40 irrelevant papers due to copied from a previous search after readings tiles, abstract and full pieces. The criteria for inclusion were: (i) articles focused on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures,” and (ii) the search key terms related to study requirements. The complete procedure yielded 55 articles for our study. We repeat our search on the “Web of Science and Google Scholars” database to enhance the search results and check the referenced articles.

In this study, 55 articles are reviewed systematically and analyzed for research topics and other aspects, such as the methods, contexts, and theories used in these studies. Furthermore, this study analyzes closely related areas to provide unique research opportunities in the future. The study also discussed future direction opportunities and research questions by understanding the research findings climate changes and other affected sectors. The reviewed paper framework analysis process is outlined in Fig.  2 .

Framework of the analysis Process.

Natural disasters and climate change’s socio-economic consequences

Natural and environmental disasters can be highly variable from year to year; some years pass with very few deaths before a significant disaster event claims many lives (Symanski et al.  2021 ). Approximately 60,000 people globally died from natural disasters each year on average over the past decade (Ritchie and Roser  2014 ; Wiranata and Simbolon  2021 ). So, according to the report, around 0.1% of global deaths. Annual variability in the number and share of deaths from natural disasters in recent decades are shown in Fig.  3 . The number of fatalities can be meager—sometimes less than 10,000, and as few as 0.01% of all deaths. But shock events have a devastating impact: the 1983–1985 famine and drought in Ethiopia; the 2004 Indian Ocean earthquake and tsunami; Cyclone Nargis, which struck Myanmar in 2008; and the 2010 Port-au-Prince earthquake in Haiti and now recent example is COVID-19 pandemic (Erman et al.  2021 ). These events pushed global disaster deaths to over 200,000—more than 0.4% of deaths in these years. Low-frequency, high-impact events such as earthquakes and tsunamis are not preventable, but such high losses of human life are. Historical evidence shows that earlier disaster detection, more robust infrastructure, emergency preparedness, and response programmers have substantially reduced disaster deaths worldwide. Low-income is also the most vulnerable to disasters; improving living conditions, facilities, and response services in these areas would be critical in reducing natural disaster deaths in the coming decades.

Global deaths from natural disasters, 1978 to 2020.

Source EMDAT ( 2020 )

The interior regions of the continent are likely to be impacted by rising temperatures (Dimri et al.  2018 ; Goes et al.  2020 ; Mannig et al.  2018 ; Schuurmans  2021 ). Weather patterns change due to the shortage of natural resources (water), increase in glacier melting, and rising mercury are likely to cause extinction to many planted species (Gampe et al.  2016 ; Mihiretu et al.  2021 ; Shaffril et al.  2018 ).On the other hand, the coastal ecosystem is on the verge of devastation (Perera et al.  2018 ; Phillips  2018 ). The temperature rises, insect disease outbreaks, health-related problems, and seasonal and lifestyle changes are persistent, with a strong probability of these patterns continuing in the future (Abbass et al. 2021c ; Hussain et al.  2018 ). At the global level, a shortage of good infrastructure and insufficient adaptive capacity are hammering the most (IPCC  2013 ). In addition to the above concerns, a lack of environmental education and knowledge, outdated consumer behavior, a scarcity of incentives, a lack of legislation, and the government’s lack of commitment to climate change contribute to the general public’s concerns. By 2050, a 2 to 3% rise in mercury and a drastic shift in rainfall patterns may have serious consequences (Huang et al. 2022 ; Gorst et al.  2018 ). Natural and environmental calamities caused huge losses globally, such as decreased agriculture outputs, rehabilitation of the system, and rebuilding necessary technologies (Ali and Erenstein  2017 ; Ramankutty et al.  2018 ; Yu et al.  2021 ) (Table ​ (Table1). 1 ). Furthermore, in the last 3 or 4 years, the world has been plagued by smog-related eye and skin diseases, as well as a rise in road accidents due to poor visibility.

Main natural danger statistics for 1985–2020 at the global level

Source: EM-DAT ( 2020 )

Climate change and agriculture

Global agriculture is the ultimate sector responsible for 30–40% of all greenhouse emissions, which makes it a leading industry predominantly contributing to climate warming and significantly impacted by it (Grieg; Mishra et al.  2021 ; Ortiz et al.  2021 ; Thornton and Lipper  2014 ). Numerous agro-environmental and climatic factors that have a dominant influence on agriculture productivity (Pautasso et al.  2012 ) are significantly impacted in response to precipitation extremes including floods, forest fires, and droughts (Huang  2004 ). Besides, the immense dependency on exhaustible resources also fuels the fire and leads global agriculture to become prone to devastation. Godfray et al. ( 2010 ) mentioned that decline in agriculture challenges the farmer’s quality of life and thus a significant factor to poverty as the food and water supplies are critically impacted by CC (Ortiz et al.  2021 ; Rosenzweig et al.  2014 ). As an essential part of the economic systems, especially in developing countries, agricultural systems affect the overall economy and potentially the well-being of households (Schlenker and Roberts  2009 ). According to the report published by the Intergovernmental Panel on Climate Change (IPCC), atmospheric concentrations of greenhouse gases, i.e., CH 4, CO 2 , and N 2 O, are increased in the air to extraordinary levels over the last few centuries (Usman and Makhdum 2021 ; Stocker et al.  2013 ). Climate change is the composite outcome of two different factors. The first is the natural causes, and the second is the anthropogenic actions (Karami 2012 ). It is also forecasted that the world may experience a typical rise in temperature stretching from 1 to 3.7 °C at the end of this century (Pachauri et al. 2014 ). The world’s crop production is also highly vulnerable to these global temperature-changing trends as raised temperatures will pose severe negative impacts on crop growth (Reidsma et al. 2009 ). Some of the recent modeling about the fate of global agriculture is briefly described below.

Decline in cereal productivity

Crop productivity will also be affected dramatically in the next few decades due to variations in integral abiotic factors such as temperature, solar radiation, precipitation, and CO 2 . These all factors are included in various regulatory instruments like progress and growth, weather-tempted changes, pest invasions (Cammell and Knight 1992 ), accompanying disease snags (Fand et al. 2012 ), water supplies (Panda et al. 2003 ), high prices of agro-products in world’s agriculture industry, and preeminent quantity of fertilizer consumption. Lobell and field ( 2007 ) claimed that from 1962 to 2002, wheat crop output had condensed significantly due to rising temperatures. Therefore, during 1980–2011, the common wheat productivity trends endorsed extreme temperature events confirmed by Gourdji et al. ( 2013 ) around South Asia, South America, and Central Asia. Various other studies (Asseng, Cao, Zhang, and Ludwig 2009 ; Asseng et al. 2013 ; García et al. 2015 ; Ortiz et al. 2021 ) also proved that wheat output is negatively affected by the rising temperatures and also caused adverse effects on biomass productivity (Calderini et al. 1999 ; Sadras and Slafer 2012 ). Hereafter, the rice crop is also influenced by the high temperatures at night. These difficulties will worsen because the temperature will be rising further in the future owing to CC (Tebaldi et al. 2006 ). Another research conducted in China revealed that a 4.6% of rice production per 1 °C has happened connected with the advancement in night temperatures (Tao et al. 2006 ). Moreover, the average night temperature growth also affected rice indicia cultivar’s output pragmatically during 25 years in the Philippines (Peng et al. 2004 ). It is anticipated that the increase in world average temperature will also cause a substantial reduction in yield (Hatfield et al. 2011 ; Lobell and Gourdji 2012 ). In the southern hemisphere, Parry et al. ( 2007 ) noted a rise of 1–4 °C in average daily temperatures at the end of spring season unti the middle of summers, and this raised temperature reduced crop output by cutting down the time length for phenophases eventually reduce the yield (Hatfield and Prueger 2015 ; R. Ortiz 2008 ). Also, world climate models have recommended that humid and subtropical regions expect to be plentiful prey to the upcoming heat strokes (Battisti and Naylor 2009 ). Grain production is the amalgamation of two constituents: the average weight and the grain output/m 2 , however, in crop production. Crop output is mainly accredited to the grain quantity (Araus et al. 2008 ; Gambín and Borrás 2010 ). In the times of grain set, yield resources are mainly strewn between hitherto defined components, i.e., grain usual weight and grain output, which presents a trade-off between them (Gambín and Borrás 2010 ) beside disparities in per grain integration (B. L. Gambín et al. 2006 ). In addition to this, the maize crop is also susceptible to raised temperatures, principally in the flowering stage (Edreira and Otegui 2013 ). In reality, the lower grain number is associated with insufficient acclimatization due to intense photosynthesis and higher respiration and the high-temperature effect on the reproduction phenomena (Edreira and Otegui 2013 ). During the flowering phase, maize visible to heat (30–36 °C) seemed less anthesis-silking intermissions (Edreira et al. 2011 ). Another research by Dupuis and Dumas ( 1990 ) proved that a drop in spikelet when directly visible to high temperatures above 35 °C in vitro pollination. Abnormalities in kernel number claimed by Vega et al. ( 2001 ) is related to conceded plant development during a flowering phase that is linked with the active ear growth phase and categorized as a critical phase for approximation of kernel number during silking (Otegui and Bonhomme 1998 ).

The retort of rice output to high temperature presents disparities in flowering patterns, and seed set lessens and lessens grain weight (Qasim et al. 2020 ; Qasim, Hammad, Maqsood, Tariq, & Chawla). During the daytime, heat directly impacts flowers which lessens the thesis period and quickens the earlier peak flowering (Tao et al. 2006 ). Antagonistic effect of higher daytime temperature d on pollen sprouting proposed seed set decay, whereas, seed set was lengthily reduced than could be explicated by pollen growing at high temperatures 40◦C (Matsui et al. 2001 ).

The decline in wheat output is linked with higher temperatures, confirmed in numerous studies (Semenov 2009 ; Stone and Nicolas 1994 ). High temperatures fast-track the arrangements of plant expansion (Blum et al. 2001 ), diminution photosynthetic process (Salvucci and Crafts‐Brandner 2004 ), and also considerably affect the reproductive operations (Farooq et al. 2011 ).

The destructive impacts of CC induced weather extremes to deteriorate the integrity of crops (Chaudhary et al. 2011 ), e.g., Spartan cold and extreme fog cause falling and discoloration of betel leaves (Rosenzweig et al. 2001 ), giving them a somehow reddish appearance, squeezing of lemon leaves (Pautasso et al. 2012 ), as well as root rot of pineapple, have reported (Vedwan and Rhoades 2001 ). Henceforth, in tackling the disruptive effects of CC, several short-term and long-term management approaches are the crucial need of time (Fig.  4 ). Moreover, various studies (Chaudhary et al. 2011 ; Patz et al. 2005 ; Pautasso et al. 2012 ) have demonstrated adapting trends such as ameliorating crop diversity can yield better adaptability towards CC.

Schematic description of potential impacts of climate change on the agriculture sector and the appropriate mitigation and adaptation measures to overcome its impact.

Climate change impacts on biodiversity

Global biodiversity is among the severe victims of CC because it is the fastest emerging cause of species loss. Studies demonstrated that the massive scale species dynamics are considerably associated with diverse climatic events (Abraham and Chain 1988 ; Manes et al. 2021 ; A. M. D. Ortiz et al. 2021 ). Both the pace and magnitude of CC are altering the compatible habitat ranges for living entities of marine, freshwater, and terrestrial regions. Alterations in general climate regimes influence the integrity of ecosystems in numerous ways, such as variation in the relative abundance of species, range shifts, changes in activity timing, and microhabitat use (Bates et al. 2014 ). The geographic distribution of any species often depends upon its ability to tolerate environmental stresses, biological interactions, and dispersal constraints. Hence, instead of the CC, the local species must only accept, adapt, move, or face extinction (Berg et al. 2010 ). So, the best performer species have a better survival capacity for adjusting to new ecosystems or a decreased perseverance to survive where they are already situated (Bates et al. 2014 ). An important aspect here is the inadequate habitat connectivity and access to microclimates, also crucial in raising the exposure to climate warming and extreme heatwave episodes. For example, the carbon sequestration rates are undergoing fluctuations due to climate-driven expansion in the range of global mangroves (Cavanaugh et al. 2014 ).

Similarly, the loss of kelp-forest ecosystems in various regions and its occupancy by the seaweed turfs has set the track for elevated herbivory by the high influx of tropical fish populations. Not only this, the increased water temperatures have exacerbated the conditions far away from the physiological tolerance level of the kelp communities (Vergés et al. 2016 ; Wernberg et al. 2016 ). Another pertinent danger is the devastation of keystone species, which even has more pervasive effects on the entire communities in that habitat (Zarnetske et al. 2012 ). It is particularly important as CC does not specify specific populations or communities. Eventually, this CC-induced redistribution of species may deteriorate carbon storage and the net ecosystem productivity (Weed et al. 2013 ). Among the typical disruptions, the prominent ones include impacts on marine and terrestrial productivity, marine community assembly, and the extended invasion of toxic cyanobacteria bloom (Fossheim et al. 2015 ).

The CC-impacted species extinction is widely reported in the literature (Beesley et al. 2019 ; Urban 2015 ), and the predictions of demise until the twenty-first century are dreadful (Abbass et al. 2019 ; Pereira et al. 2013 ). In a few cases, northward shifting of species may not be formidable as it allows mountain-dwelling species to find optimum climates. However, the migrant species may be trapped in isolated and incompatible habitats due to losing topography and range (Dullinger et al. 2012 ). For example, a study indicated that the American pika has been extirpated or intensely diminished in some regions, primarily attributed to the CC-impacted extinction or at least local extirpation (Stewart et al. 2015 ). Besides, the anticipation of persistent responses to the impacts of CC often requires data records of several decades to rigorously analyze the critical pre and post CC patterns at species and ecosystem levels (Manes et al. 2021 ; Testa et al. 2018 ).

Nonetheless, the availability of such long-term data records is rare; hence, attempts are needed to focus on these profound aspects. Biodiversity is also vulnerable to the other associated impacts of CC, such as rising temperatures, droughts, and certain invasive pest species. For instance, a study revealed the changes in the composition of plankton communities attributed to rising temperatures. Henceforth, alterations in such aquatic producer communities, i.e., diatoms and calcareous plants, can ultimately lead to variation in the recycling of biological carbon. Moreover, such changes are characterized as a potential contributor to CO 2 differences between the Pleistocene glacial and interglacial periods (Kohfeld et al. 2005 ).

Climate change implications on human health

It is an understood corporality that human health is a significant victim of CC (Costello et al. 2009 ). According to the WHO, CC might be responsible for 250,000 additional deaths per year during 2030–2050 (Watts et al. 2015 ). These deaths are attributed to extreme weather-induced mortality and morbidity and the global expansion of vector-borne diseases (Lemery et al. 2021; Yang and Usman 2021 ; Meierrieks 2021 ; UNEP 2017 ). Here, some of the emerging health issues pertinent to this global problem are briefly described.

Climate change and antimicrobial resistance with corresponding economic costs

Antimicrobial resistance (AMR) is an up-surging complex global health challenge (Garner et al. 2019 ; Lemery et al. 2021 ). Health professionals across the globe are extremely worried due to this phenomenon that has critical potential to reverse almost all the progress that has been achieved so far in the health discipline (Gosling and Arnell 2016 ). A massive amount of antibiotics is produced by many pharmaceutical industries worldwide, and the pathogenic microorganisms are gradually developing resistance to them, which can be comprehended how strongly this aspect can shake the foundations of national and global economies (UNEP 2017 ). This statement is supported by the fact that AMR is not developing in a particular region or country. Instead, it is flourishing in every continent of the world (WHO 2018 ). This plague is heavily pushing humanity to the post-antibiotic era, in which currently antibiotic-susceptible pathogens will once again lead to certain endemics and pandemics after being resistant(WHO 2018 ). Undesirably, if this statement would become a factuality, there might emerge certain risks in undertaking sophisticated interventions such as chemotherapy, joint replacement cases, and organ transplantation (Su et al. 2018 ). Presently, the amplification of drug resistance cases has made common illnesses like pneumonia, post-surgical infections, HIV/AIDS, tuberculosis, malaria, etc., too difficult and costly to be treated or cure well (WHO 2018 ). From a simple example, it can be assumed how easily antibiotic-resistant strains can be transmitted from one person to another and ultimately travel across the boundaries (Berendonk et al. 2015 ). Talking about the second- and third-generation classes of antibiotics, e.g., most renowned generations of cephalosporin antibiotics that are more expensive, broad-spectrum, more toxic, and usually require more extended periods whenever prescribed to patients (Lemery et al. 2021 ; Pärnänen et al. 2019 ). This scenario has also revealed that the abundance of resistant strains of pathogens was also higher in the Southern part (WHO 2018 ). As southern parts are generally warmer than their counterparts, it is evident from this example how CC-induced global warming can augment the spread of antibiotic-resistant strains within the biosphere, eventually putting additional economic burden in the face of developing new and costlier antibiotics. The ARG exchange to susceptible bacteria through one of the potential mechanisms, transformation, transduction, and conjugation; Selection pressure can be caused by certain antibiotics, metals or pesticides, etc., as shown in Fig.  5 .

A typical interaction between the susceptible and resistant strains.

Source: Elsayed et al. ( 2021 ); Karkman et al. ( 2018 )

Certain studies highlighted that conventional urban wastewater treatment plants are typical hotspots where most bacterial strains exchange genetic material through horizontal gene transfer (Fig.  5 ). Although at present, the extent of risks associated with the antibiotic resistance found in wastewater is complicated; environmental scientists and engineers have particular concerns about the potential impacts of these antibiotic resistance genes on human health (Ashbolt 2015 ). At most undesirable and worst case, these antibiotic-resistant genes containing bacteria can make their way to enter into the environment (Pruden et al. 2013 ), irrigation water used for crops and public water supplies and ultimately become a part of food chains and food webs (Ma et al. 2019 ; D. Wu et al. 2019 ). This problem has been reported manifold in several countries (Hendriksen et al. 2019 ), where wastewater as a means of irrigated water is quite common.

Climate change and vector borne-diseases

Temperature is a fundamental factor for the sustenance of living entities regardless of an ecosystem. So, a specific living being, especially a pathogen, requires a sophisticated temperature range to exist on earth. The second essential component of CC is precipitation, which also impacts numerous infectious agents’ transport and dissemination patterns. Global rising temperature is a significant cause of many species extinction. On the one hand, this changing environmental temperature may be causing species extinction, and on the other, this warming temperature might favor the thriving of some new organisms. Here, it was evident that some pathogens may also upraise once non-evident or reported (Patz et al. 2000 ). This concept can be exemplified through certain pathogenic strains of microorganisms that how the likelihood of various diseases increases in response to climate warming-induced environmental changes (Table ​ (Table2 2 ).

Examples of how various environmental changes affect various infectious diseases in humans

Source: Aron and Patz ( 2001 )

A recent example is an outburst of coronavirus (COVID-19) in the Republic of China, causing pneumonia and severe acute respiratory complications (Cui et al. 2021 ; Song et al. 2021 ). The large family of viruses is harbored in numerous animals, bats, and snakes in particular (livescience.com) with the subsequent transfer into human beings. Hence, it is worth noting that the thriving of numerous vectors involved in spreading various diseases is influenced by Climate change (Ogden 2018 ; Santos et al. 2021 ).

Psychological impacts of climate change

Climate change (CC) is responsible for the rapid dissemination and exaggeration of certain epidemics and pandemics. In addition to the vast apparent impacts of climate change on health, forestry, agriculture, etc., it may also have psychological implications on vulnerable societies. It can be exemplified through the recent outburst of (COVID-19) in various countries around the world (Pal 2021 ). Besides, the victims of this viral infection have made healthy beings scarier and terrified. In the wake of such epidemics, people with common colds or fever are also frightened and must pass specific regulatory protocols. Living in such situations continuously terrifies the public and makes the stress familiar, which eventually makes them psychologically weak (npr.org).

CC boosts the extent of anxiety, distress, and other issues in public, pushing them to develop various mental-related problems. Besides, frequent exposure to extreme climatic catastrophes such as geological disasters also imprints post-traumatic disorder, and their ubiquitous occurrence paves the way to developing chronic psychological dysfunction. Moreover, repetitive listening from media also causes an increase in the person’s stress level (Association 2020 ). Similarly, communities living in flood-prone areas constantly live in extreme fear of drowning and die by floods. In addition to human lives, the flood-induced destruction of physical infrastructure is a specific reason for putting pressure on these communities (Ogden 2018 ). For instance, Ogden ( 2018 ) comprehensively denoted that Katrina’s Hurricane augmented the mental health issues in the victim communities.

Climate change impacts on the forestry sector

Forests are the global regulators of the world’s climate (FAO 2018 ) and have an indispensable role in regulating global carbon and nitrogen cycles (Rehman et al. 2021 ; Reichstein and Carvalhais 2019 ). Hence, disturbances in forest ecology affect the micro and macro-climates (Ellison et al. 2017 ). Climate warming, in return, has profound impacts on the growth and productivity of transboundary forests by influencing the temperature and precipitation patterns, etc. As CC induces specific changes in the typical structure and functions of ecosystems (Zhang et al. 2017 ) as well impacts forest health, climate change also has several devastating consequences such as forest fires, droughts, pest outbreaks (EPA 2018 ), and last but not the least is the livelihoods of forest-dependent communities. The rising frequency and intensity of another CC product, i.e., droughts, pose plenty of challenges to the well-being of global forests (Diffenbaugh et al. 2017 ), which is further projected to increase soon (Hartmann et al. 2018 ; Lehner et al. 2017 ; Rehman et al. 2021 ). Hence, CC induces storms, with more significant impacts also put extra pressure on the survival of the global forests (Martínez-Alvarado et al. 2018 ), significantly since their influences are augmented during higher winter precipitations with corresponding wetter soils causing weak root anchorage of trees (Brázdil et al. 2018 ). Surging temperature regimes causes alterations in usual precipitation patterns, which is a significant hurdle for the survival of temperate forests (Allen et al. 2010 ; Flannigan et al. 2013 ), letting them encounter severe stress and disturbances which adversely affects the local tree species (Hubbart et al. 2016 ; Millar and Stephenson 2015 ; Rehman et al. 2021 ).

Climate change impacts on forest-dependent communities

Forests are the fundamental livelihood resource for about 1.6 billion people worldwide; out of them, 350 million are distinguished with relatively higher reliance (Bank 2008 ). Agro-forestry-dependent communities comprise 1.2 billion, and 60 million indigenous people solely rely on forests and their products to sustain their lives (Sunderlin et al. 2005 ). For example, in the entire African continent, more than 2/3rd of inhabitants depend on forest resources and woodlands for their alimonies, e.g., food, fuelwood and grazing (Wasiq and Ahmad 2004 ). The livings of these people are more intensely affected by the climatic disruptions making their lives harder (Brown et al. 2014 ). On the one hand, forest communities are incredibly vulnerable to CC due to their livelihoods, cultural and spiritual ties as well as socio-ecological connections, and on the other, they are not familiar with the term “climate change.” (Rahman and Alam 2016 ). Among the destructive impacts of temperature and rainfall, disruption of the agroforestry crops with resultant downscale growth and yield (Macchi et al. 2008 ). Cruz ( 2015 ) ascribed that forest-dependent smallholder farmers in the Philippines face the enigma of delayed fruiting, more severe damages by insect and pest incidences due to unfavorable temperature regimes, and changed rainfall patterns.

Among these series of challenges to forest communities, their well-being is also distinctly vulnerable to CC. Though the detailed climate change impacts on human health have been comprehensively mentioned in the previous section, some studies have listed a few more devastating effects on the prosperity of forest-dependent communities. For instance, the Himalayan people have been experiencing frequent skin-borne diseases such as malaria and other skin diseases due to increasing mosquitoes, wild boar as well, and new wasps species, particularly in higher altitudes that were almost non-existent before last 5–10 years (Xu et al. 2008 ). Similarly, people living at high altitudes in Bangladesh have experienced frequent mosquito-borne calamities (Fardous; Sharma 2012 ). In addition, the pace of other waterborne diseases such as infectious diarrhea, cholera, pathogenic induced abdominal complications and dengue has also been boosted in other distinguished regions of Bangladesh (Cell 2009 ; Gunter et al. 2008 ).

Pest outbreak

Upscaling hotter climate may positively affect the mobile organisms with shorter generation times because they can scurry from harsh conditions than the immobile species (Fettig et al. 2013 ; Schoene and Bernier 2012 ) and are also relatively more capable of adapting to new environments (Jactel et al. 2019 ). It reveals that insects adapt quickly to global warming due to their mobility advantages. Due to past outbreaks, the trees (forests) are relatively more susceptible victims (Kurz et al. 2008 ). Before CC, the influence of factors mentioned earlier, i.e., droughts and storms, was existent and made the forests susceptible to insect pest interventions; however, the global forests remain steadfast, assiduous, and green (Jactel et al. 2019 ). The typical reasons could be the insect herbivores were regulated by several tree defenses and pressures of predation (Wilkinson and Sherratt 2016 ). As climate greatly influences these phenomena, the global forests cannot be so sedulous against such challenges (Jactel et al. 2019 ). Table ​ Table3 3 demonstrates some of the particular considerations with practical examples that are essential while mitigating the impacts of CC in the forestry sector.

Essential considerations while mitigating the climate change impacts on the forestry sector

Source : Fischer ( 2019 )

Climate change impacts on tourism

Tourism is a commercial activity that has roots in multi-dimensions and an efficient tool with adequate job generation potential, revenue creation, earning of spectacular foreign exchange, enhancement in cross-cultural promulgation and cooperation, a business tool for entrepreneurs and eventually for the country’s national development (Arshad et al. 2018 ; Scott 2021 ). Among a plethora of other disciplines, the tourism industry is also a distinct victim of climate warming (Gössling et al. 2012 ; Hall et al. 2015 ) as the climate is among the essential resources that enable tourism in particular regions as most preferred locations. Different places at different times of the year attract tourists both within and across the countries depending upon the feasibility and compatibility of particular weather patterns. Hence, the massive variations in these weather patterns resulting from CC will eventually lead to monumental challenges to the local economy in that specific area’s particular and national economy (Bujosa et al. 2015 ). For instance, the Intergovernmental Panel on Climate Change (IPCC) report demonstrated that the global tourism industry had faced a considerable decline in the duration of ski season, including the loss of some ski areas and the dramatic shifts in tourist destinations’ climate warming.

Furthermore, different studies (Neuvonen et al. 2015 ; Scott et al. 2004 ) indicated that various currently perfect tourist spots, e.g., coastal areas, splendid islands, and ski resorts, will suffer consequences of CC. It is also worth noting that the quality and potential of administrative management potential to cope with the influence of CC on the tourism industry is of crucial significance, which renders specific strengths of resiliency to numerous destinations to withstand against it (Füssel and Hildén 2014 ). Similarly, in the partial or complete absence of adequate socio-economic and socio-political capital, the high-demanding tourist sites scurry towards the verge of vulnerability. The susceptibility of tourism is based on different components such as the extent of exposure, sensitivity, life-supporting sectors, and capacity assessment factors (Füssel and Hildén 2014 ). It is obvious corporality that sectors such as health, food, ecosystems, human habitat, infrastructure, water availability, and the accessibility of a particular region are prone to CC. Henceforth, the sensitivity of these critical sectors to CC and, in return, the adaptive measures are a hallmark in determining the composite vulnerability of climate warming (Ionescu et al. 2009 ).

Moreover, the dependence on imported food items, poor hygienic conditions, and inadequate health professionals are dominant aspects affecting the local terrestrial and aquatic biodiversity. Meanwhile, the greater dependency on ecosystem services and its products also makes a destination more fragile to become a prey of CC (Rizvi et al. 2015 ). Some significant non-climatic factors are important indicators of a particular ecosystem’s typical health and functioning, e.g., resource richness and abundance portray the picture of ecosystem stability. Similarly, the species abundance is also a productive tool that ensures that the ecosystem has a higher buffering capacity, which is terrific in terms of resiliency (Roscher et al. 2013 ).

Climate change impacts on the economic sector

Climate plays a significant role in overall productivity and economic growth. Due to its increasingly global existence and its effect on economic growth, CC has become one of the major concerns of both local and international environmental policymakers (Ferreira et al. 2020 ; Gleditsch 2021 ; Abbass et al. 2021b ; Lamperti et al. 2021 ). The adverse effects of CC on the overall productivity factor of the agricultural sector are therefore significant for understanding the creation of local adaptation policies and the composition of productive climate policy contracts. Previous studies on CC in the world have already forecasted its effects on the agricultural sector. Researchers have found that global CC will impact the agricultural sector in different world regions. The study of the impacts of CC on various agrarian activities in other demographic areas and the development of relative strategies to respond to effects has become a focal point for researchers (Chandioet al. 2020 ; Gleditsch 2021 ; Mosavi et al. 2020 ).

With the rapid growth of global warming since the 1980s, the temperature has started increasing globally, which resulted in the incredible transformation of rain and evaporation in the countries. The agricultural development of many countries has been reliant, delicate, and susceptible to CC for a long time, and it is on the development of agriculture total factor productivity (ATFP) influence different crops and yields of farmers (Alhassan 2021 ; Wu  2020 ).

Food security and natural disasters are increasing rapidly in the world. Several major climatic/natural disasters have impacted local crop production in the countries concerned. The effects of these natural disasters have been poorly controlled by the development of the economies and populations and may affect human life as well. One example is China, which is among the world’s most affected countries, vulnerable to natural disasters due to its large population, harsh environmental conditions, rapid CC, low environmental stability, and disaster power. According to the January 2016 statistical survey, China experienced an economic loss of 298.3 billion Yuan, and about 137 million Chinese people were severely affected by various natural disasters (Xie et al. 2018 ).

Mitigation and adaptation strategies of climate changes

Adaptation and mitigation are the crucial factors to address the response to CC (Jahanzad et al. 2020 ). Researchers define mitigation on climate changes, and on the other hand, adaptation directly impacts climate changes like floods. To some extent, mitigation reduces or moderates greenhouse gas emission, and it becomes a critical issue both economically and environmentally (Botzen et al. 2021 ; Jahanzad et al. 2020 ; Kongsager 2018 ; Smit et al. 2000 ; Vale et al. 2021 ; Usman et al. 2021 ; Verheyen 2005 ).

Researchers have deep concern about the adaptation and mitigation methodologies in sectoral and geographical contexts. Agriculture, industry, forestry, transport, and land use are the main sectors to adapt and mitigate policies(Kärkkäinen et al. 2020 ; Waheed et al. 2021 ). Adaptation and mitigation require particular concern both at the national and international levels. The world has faced a significant problem of climate change in the last decades, and adaptation to these effects is compulsory for economic and social development. To adapt and mitigate against CC, one should develop policies and strategies at the international level (Hussain et al. 2020 ). Figure  6 depicts the list of current studies on sectoral impacts of CC with adaptation and mitigation measures globally.

Sectoral impacts of climate change with adaptation and mitigation measures.

Conclusion and future perspectives

Specific socio-agricultural, socio-economic, and physical systems are the cornerstone of psychological well-being, and the alteration in these systems by CC will have disastrous impacts. Climate variability, alongside other anthropogenic and natural stressors, influences human and environmental health sustainability. Food security is another concerning scenario that may lead to compromised food quality, higher food prices, and inadequate food distribution systems. Global forests are challenged by different climatic factors such as storms, droughts, flash floods, and intense precipitation. On the other hand, their anthropogenic wiping is aggrandizing their existence. Undoubtedly, the vulnerability scale of the world’s regions differs; however, appropriate mitigation and adaptation measures can aid the decision-making bodies in developing effective policies to tackle its impacts. Presently, modern life on earth has tailored to consistent climatic patterns, and accordingly, adapting to such considerable variations is of paramount importance. Because the faster changes in climate will make it harder to survive and adjust, this globally-raising enigma calls for immediate attention at every scale ranging from elementary community level to international level. Still, much effort, research, and dedication are required, which is the most critical time. Some policy implications can help us to mitigate the consequences of climate change, especially the most affected sectors like the agriculture sector;

Warming might lengthen the season in frost-prone growing regions (temperate and arctic zones), allowing for longer-maturing seasonal cultivars with better yields (Pfadenhauer 2020 ; Bonacci 2019 ). Extending the planting season may allow additional crops each year; when warming leads to frequent warmer months highs over critical thresholds, a split season with a brief summer fallow may be conceivable for short-period crops such as wheat barley, cereals, and many other vegetable crops. The capacity to prolong the planting season in tropical and subtropical places where the harvest season is constrained by precipitation or agriculture farming occurs after the year may be more limited and dependent on how precipitation patterns vary (Wu et al. 2017 ).

The genetic component is comprehensive for many yields, but it is restricted like kiwi fruit for a few. Ali et al. ( 2017 ) investigated how new crops will react to climatic changes (also stated in Mall et al. 2017 ). Hot temperature, drought, insect resistance; salt tolerance; and overall crop production and product quality increases would all be advantageous (Akkari 2016 ). Genetic mapping and engineering can introduce a greater spectrum of features. The adoption of genetically altered cultivars has been slowed, particularly in the early forecasts owing to the complexity in ensuring features are expediently expressed throughout the entire plant, customer concerns, economic profitability, and regulatory impediments (Wirehn 2018 ; Davidson et al. 2016 ).

To get the full benefit of the CO 2 would certainly require additional nitrogen and other fertilizers. Nitrogen not consumed by the plants may be excreted into groundwater, discharged into water surface, or emitted from the land, soil nitrous oxide when large doses of fertilizer are sprayed. Increased nitrogen levels in groundwater sources have been related to human chronic illnesses and impact marine ecosystems. Cultivation, grain drying, and other field activities have all been examined in depth in the studies (Barua et al. 2018 ).

• The technological and socio-economic adaptation

The policy consequence of the causative conclusion is that as a source of alternative energy, biofuel production is one of the routes that explain oil price volatility separate from international macroeconomic factors. Even though biofuel production has just begun in a few sample nations, there is still a tremendous worldwide need for feedstock to satisfy industrial expansion in China and the USA, which explains the food price relationship to the global oil price. Essentially, oil-exporting countries may create incentives in their economies to increase food production. It may accomplish by giving farmers financing, seedlings, fertilizers, and farming equipment. Because of the declining global oil price and, as a result, their earnings from oil export, oil-producing nations may be unable to subsidize food imports even in the near term. As a result, these countries can boost the agricultural value chain for export. It may be accomplished through R&D and adding value to their food products to increase income by correcting exchange rate misalignment and adverse trade terms. These nations may also diversify their economies away from oil, as dependence on oil exports alone is no longer economically viable given the extreme volatility of global oil prices. Finally, resource-rich and oil-exporting countries can convert to non-food renewable energy sources such as solar, hydro, coal, wind, wave, and tidal energy. By doing so, both world food and oil supplies would be maintained rather than harmed.

IRENA’s modeling work shows that, if a comprehensive policy framework is in place, efforts toward decarbonizing the energy future will benefit economic activity, jobs (outweighing losses in the fossil fuel industry), and welfare. Countries with weak domestic supply chains and a large reliance on fossil fuel income, in particular, must undertake structural reforms to capitalize on the opportunities inherent in the energy transition. Governments continue to give major policy assistance to extract fossil fuels, including tax incentives, financing, direct infrastructure expenditures, exemptions from environmental regulations, and other measures. The majority of major oil and gas producing countries intend to increase output. Some countries intend to cut coal output, while others plan to maintain or expand it. While some nations are beginning to explore and execute policies aimed at a just and equitable transition away from fossil fuel production, these efforts have yet to impact major producing countries’ plans and goals. Verifiable and comparable data on fossil fuel output and assistance from governments and industries are critical to closing the production gap. Governments could increase openness by declaring their production intentions in their climate obligations under the Paris Agreement.

It is firmly believed that achieving the Paris Agreement commitments is doubtlful without undergoing renewable energy transition across the globe (Murshed 2020 ; Zhao et al. 2022 ). Policy instruments play the most important role in determining the degree of investment in renewable energy technology. This study examines the efficacy of various policy strategies in the renewable energy industry of multiple nations. Although its impact is more visible in established renewable energy markets, a renewable portfolio standard is also a useful policy instrument. The cost of producing renewable energy is still greater than other traditional energy sources. Furthermore, government incentives in the R&D sector can foster innovation in this field, resulting in cost reductions in the renewable energy industry. These nations may export their technologies and share their policy experiences by forming networks among their renewable energy-focused organizations. All policy measures aim to reduce production costs while increasing the proportion of renewables to a country’s energy system. Meanwhile, long-term contracts with renewable energy providers, government commitment and control, and the establishment of long-term goals can assist developing nations in deploying renewable energy technology in their energy sector.

Author contribution

KA: Writing the original manuscript, data collection, data analysis, Study design, Formal analysis, Visualization, Revised draft, Writing-review, and editing. MZQ: Writing the original manuscript, data collection, data analysis, Writing-review, and editing. HS: Contribution to the contextualization of the theme, Conceptualization, Validation, Supervision, literature review, Revised drapt, and writing review and editing. MM: Writing review and editing, compiling the literature review, language editing. HM: Writing review and editing, compiling the literature review, language editing. IY: Contribution to the contextualization of the theme, literature review, and writing review and editing.

Availability of data and material

Declarations.

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The authors declare no competing interests.

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Contributor Information

Kashif Abbass, Email: nc.ude.tsujn@ssabbafihsak .

Muhammad Zeeshan Qasim, Email: moc.kooltuo@888misaqnahseez .

Huaming Song, Email: nc.ude.tsujn@gnimauh .

Muntasir Murshed, Email: [email protected] .

Haider Mahmood, Email: moc.liamtoh@doomhamrediah .

Ijaz Younis, Email: nc.ude.tsujn@sinuoyzaji .

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