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Hydrological impacts of climate change are assessed by downscaling the General Circulation Model (GCM) outputs of predictor variables to local or regional scale hydrologic variables (predictand). Support Vector Machine (SVM) is a machine learning technique which is capable of capturing highly nonlinear relationship between predictor and predictand and thus performs better than conventional linear regression in transfer function‐based downscaling modeling. SVM has certain parameters the values of which need to be fixed appropriately for controlling undertraining and overtraining. In this study, an optimization model is proposed to estimate the values of these parameters. As the optimization model, for selection of parameters, contains SVM as one of its constraints, analytical solution techniques are difficult to use in solving it. Probabilistic Global Search Algorithm (PGSL), a probabilistic search technique, is used to compute the optimum parameters of SVM. With these optimum parameters, training of SVM is performed for statistical downscaling. The obtained relationship between large‐scale atmospheric variables and local‐scale hydrologic variables (e.g., rainfall) is used to compute the hydrologic scenarios for multiple GCMs. The uncertainty resulting from the use of multiple GCMs is further modeled with a modified reliability ensemble averaging method. The proposed methodology is demonstrated with the prediction of monsoon rainfall of Assam and Meghalaya meteorological subdivision of northeastern India. The results obtained from the proposed model are compared with earlier developed SVM‐based downscaling models, and improved performance is observed.

Atmospheric aridity (vapor pressure deficit, VPD) and soil moisture (SM) deficit limit plant photosynthesis and, thus, affect vegetation carbon uptake. The strong correlation between SM and VPD makes it challenging to delineate their relative contributions to regional vegetation productivity. Addressing this gap is vital to understand the future trajectory of plant productivity in India—the second-highest contributor to global greening. Here, we separate the controls of SM and VPD on the Indian vegetation using statistical and causal analysis. We found that vegetation productivity in India is primarily controlled by SM limitation (87.66% of grids) than VPD limitation (12.34% of grids). Vegetation has a varying association with SM and VPD across different agroecological regions in India. The negative impact of VPD on vegetation carbon uptake is not visible in high-rainfall areas of India. These findings advance our understanding of vegetation dynamics under regional dryness stress and can enhance dynamic vegetation model estimates for India under changing climate scenarios.

Increased occurrence of heatwaves across different parts of the world is one of the characteristic signatures of anthropogenic warming. With a 1.3 billion population, India is one of the hot spots that experience deadly heatwaves during May-June – yet the large-scale physical mechanism and teleconnection patterns driving such events remain poorly understood. Here using observations and controlled climate model experiments, we demonstrate a significant footprint of the far-reaching Pacific Meridional Mode (PMM) on the heatwave intensity (and duration) across North Central India (NCI) – the high risk region prone to heatwaves. A strong positive phase of PMM leads to a significant increase in heatwave intensity and duration over NCI (0.8-2 °C and 3–6 days; p  < 0.05) and vice-versa. The current generation (CMIP6) climate models that adequately capture the PMM and their responses to NCI heatwaves, project significantly higher intensities of future heatwaves (0.5-1 °C; p  < 0.05) compared to all model ensembles. These differences in the intensities of heatwaves could significantly increase the mortality (by ≈150%) and therefore can have substantial implications on designing the mitigation and adaptation strategies. New study finds that Summer Indian heatwaves are controlled by the large scale atmospheric circulation associated with the Pacific Meridional Mode.

The impact of soil moisture (SM) and vapor pressure deficit (VPD) on gross primary productivity (GPP) variability in ecosystems is a topic of significant interest. Previous studies have predominantly focused on real-time associations between SM, VPD, and carbon uptake, attributing SM as the principal driver of GPP variability due to its direct and indirect effects through VPD. Using an information theory-based process network approach, we discovered that the influence of past VPD, mediated through its effects on SM, emerges as the primary driver of GPP variability across tropical regions. The past VPD conditions influence GPP directly and also affect SM in real-time alongside GPP, which subsequently impacts GPP variability. Examining land-atmosphere feedback using information theory reveals that past VPD conditions influence SM, but not the reverse. These causal structures explain the consistent decline in GPP with increasing VPD trends observed in tropical regions, which are not consistent with SM trends. Our findings emphasize the importance of considering the influence of past VPD mediated by SM when analyzing complex land-vegetation-atmosphere interactions.

Hot extremes are anticipated to be more frequent and more intense under climate change, making the Indo-Gangetic Plain of India, with a 400 million population, vulnerable to heat stress. Recent studies suggest that irrigation has significant cooling and moistening effects over this region. While large-scale irrigation is prevalent in the Indo-Gangetic Plain during the two major cropping seasons, Kharif (Jun-Sep) and Rabi (Nov-Feb), hot extremes are reported in the pre-monsoon months (Apr-May) when irrigation activities are minimal. Here, using observed irrigation data and regional climate model simulations, we show that irrigation effects on heat stress during pre-monsoon are 4.9 times overestimated with model-simulated irrigation as prescribed in previous studies. We find that irrigation increases relative humidity by only 2.5%, indicating that irrigation is a non-crucial factor enhancing the moist heat stress. On the other hand, we detect causal effects of aerosol abundance on the daytime land surface temperature. Our study highlights the need to consider actual irrigation data in testing model-driven hypotheses related to the land-atmosphere feedback driven by human water management. Pre-monsoon irrigation over the Indo-Gangetic Plain is often misrepresented in model-driven hypothesis. Using actual census-based data and realistic model simulations, the authors show that irrigation has limited role in enhancing heat stress in the region.

Higher warming will affect more regions globally with intensified agricultural and ecological droughts. Higher CO2 concentration improves vegetation’s water use efficiency (WUE), but its potential to alleviate extreme agricultural and ecological droughts is unclear. India is the second-highest contributor to global greening, having two of the eight global hottest biodiversity hotspots. Here, for the first time, using the CMIP6 earth system models (ESMs), we found an increase in the net vegetation productivity in India at the rate of 10.552 TgC year−1 with 1% per year increase in atmospheric CO2 concentration from 285 ppm to 1140 ppm, contrary to global trends. The improved WUE resulting from carbon fertilization and higher rain under warming will supersede the increased evapotranspiration water loss due to radiative effects. We found that the substantial increase in vegetation productivity in India attributes to plant physiology, and such factor needs to be considered in the drought projections.

The difference in land surface temperature (LST) between an urban region and its nearby non–urban region, known as surface urban heat island intensity (SUHII), is usually positive as reported in earlier studies. India has experienced unprecedented urbanization over recent decades with an urban population of 380 million. Here, we present the first study of the diurnal and seasonal characteristics of SUHII in India. We found negative SUHII over a majority of urban areas during daytime in pre-monsoon summer (MAM), contrary to the expected impacts of urbanization. This unexpected pattern is associated with low vegetation in non-urban regions during dry pre-monsoon summers, leading to reduced evapotranspiration (ET). During pre-monsoon summer nights, a positive SUHII occurs when urban impacts are prominent. Winter daytime SUHII becomes positive in Indo-Gangetic plain. We attribute such diurnal and seasonal behaviour of SUHII to the same of the differences in ET between urban and non-urban regions. Higher LST in non-urban regions during pre-monsoon summer days results in intensified heatwaves compared to heatwaves in cities, in contrast to presumptions made in the literature. These observations highlight the need for re-evaluation of SUHII in India for climate adaptation, heat stress mitigation, and analysis of urban micro-climates.

Terrestrial ecosystems play a vital role in mitigating climate change by absorbing a substantial fraction of anthropogenic CO2 emissions, with net ecosystem exchange (NEE) serving as a critical metric of land-atmosphere carbon flux. While individual climate variables like temperature, precipitation, and radiation are well-studied drivers of NEE, their interactions in multivariate contexts remain poorly understood. This study leverages the O-Information framework to disentangle the synergistic and redundant contributions of temperature, precipitation, vapour pressure deficit (VPD), terrestrial water storage, and photosynthetically active radiation (PAR) to NEE variability. Analysing global and regional dynamics, we reveal the nature of multivariate interactions governing NEE. Globally, pairs like VPD-PAR consistently exhibit synergy, underscoring their complementary roles in driving carbon exchange, while T-VPD and T-terrestrial water storage interactions are predominantly redundant. Regional analyses highlight distinct patterns of information sharing: temperate forests and semiarid regions are synergy-dominated, whereas tropical ecosystems and Arctic regions exhibit unique spatial variability in synergy and redundancy. These findings advance our understanding of how complex climate-ecosystem interactions shape carbon fluxes and offer insights for improving predictive models of NEE under changing climatic conditions.

Socioeconomic challenges continue to mount for half a billion residents of central India because of a decline in the total rainfall and a concurrent rise in the magnitude and frequency of extreme rainfall events. Alongside a weakening monsoon circulation, the locally available moisture and the frequency of moisture-laden depressions from the Bay of Bengal have also declined. Here we show that despite these negative trends, there is a threefold increase in widespread extreme rain events over central India during 1950–2015. The rise in these events is due to an increasing variability of the low-level monsoon westerlies over the Arabian Sea, driving surges of moisture supply, leading to extreme rainfall episodes across the entire central subcontinent. The homogeneity of these severe weather events and their association with the ocean temperatures underscores the potential predictability of these events by two-to-three weeks, which offers hope in mitigating their catastrophic impact on life, agriculture and property. Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India. Here the authors identify a threefold increase in widespread extreme rains over the region during 1950–2015, driven by an increasing variability of the low-level westerlies over the Arabian Sea.

Anthropogenic changes are likely to intensify rainfall extremes, posing a risk to human, environmental and urban systems. Understanding the impact of urbanization on rainfall extremes is critical for both reliable climate projections as well as sustainable urban development. This study presents the unexplored impacts of changes arising in urban areas on rainfall extremes over the Contiguous United States. The results show a 2.7-fold higher probability of exceeding a 25% change in 50 year rainfall events over urban areas than over rural areas. Spatially, the changes in rainfall extremes over the central, northeast central, southeast, and northwest central zones were more pronounced due to urbanization. Statistical analyses highlight a positive relationship between changes in rainfall extremes and urbanization within a set of concentric ring buffers around rain gauge stations. Here, we show that urbanization, even though a local feature, influences the mesoscale meteorological setting; and, is statistically associated with an intensification of rainfall extremes across the Contiguous United States.