Since the beginning of the 21st century, Central Europe (CE) has experienced a series of dry summers with substantial socioeconomic and environmental impacts. While the influence of the North Atlantic Ocean is well-established, the role of the Pacific Ocean in modulating CE summer rainfall (CESR) is relatively unknown. Here, we present an evidence by demonstrating the prominent role of the Pacific Meridional Mode (PMM) on the inter-annual variability of CESR. A strong positive phase of PMM is linked to a significant rainfall deficit over CE (10%-15%; p-value < 0.05) during the boreal summer season. Further, we demonstrate the implications of this novel teleconnection pattern in improving the predictability of CESR at seasonal timescales-along with the mutually exclusive and independent North Atlantic oscillation. Our study thus reveals the far-reaching influences of the North Pacific Ocean on an improved prediction of the CESR variability, which may aid in improving the adaptation and mitigation strategies.

This study focuses on the role of human activities in shaping climate forcings and their impact on surface air temperature (SAT) and drought intensification over Africa, emphasizing the human contributions to these phenomena. Through the analysis of observations, various model experiments, and Regularized Optimal Fingerprinting detection technique, our findings indicate that human-induced factors have contributed to an increase in surface air temperatures ranging from 0.8 to 1.06^∘ C above pre-industrial benchmarks. Greenhouse gases (GHGs) emerge as the primary driver of this rise (0.47 to 0.92^∘ C), followed by land use (LU) changes (0.47 to 0.63^∘ C). In contrast, anthropogenic aerosols (Aaer) exert a cooling effect (-1.82 to -1.36^∘ C) on SAT. The analysis reveals that SAT anomalies, particularly during the industrial period, have significantly contributed to the intensification of drought-prone climatic conditions. During the pre-industrial period, the absence of anthropogenic warming kept SAT stable, resulting in mildly wet conditions (Standardized Precipitation Evapotranspiration Index (SPEI)=0.54). However, in the industrial period, the sharp rise in SAT due to GHG and LU forcings led towards significantly drought-prone climatic conditions (SPEI=-0.73), while the cooling effect of Aaer was insufficient to offset the warming trend. Estimates based on Representative Concentration Pathways (RCP) 4.5 and 8.5 suggest that the SAT over Africa could rise by around 2^∘ C and 5^∘ C, respectively, by the end of the century, highlighting the significant influence of human-driven factors in driving temperature rise. Strategic oversight of GHG emissions, LU changes, and aerosol concentrations in Africa offers the possibility potential to mitigate further warming and consequent drought intensification in this region.

Rainfed crops account for approximately 40% of India’s food production and support 60% of its livestock. Although linked to oceanic monsoon rainfall, their productivity also depends on terrestrial evaporation, particularly in the non-monsoon season. However, the degree to which rainfed crops also rely on moisture sourced from upwind irrigated areas, remains largely unknown. Using a combination of models and observations, we show that upwind irrigated crops contribute 7%±6% of the rainfall over rainfed areas, rising to 15%±10% during the pre-monsoon months. In the absence of this input, water stress experienced by rainfed crops can increase by 5–10% during the crucial mid to late crop growth phases, potentially affecting yields. Our results reveal an unrecognized atmospheric link between irrigated and rainfed agriculture, which is overlooked in current agricultural policies. Planning and managing these systems in an holistic manner can help strengthen regional food and water security under future climates.

Rainfed crops account for approximately 40% of India's food production and support 60% of its livestock. Although linked to oceanic monsoon rainfall, their productivity also depends on terrestrially-sourced rainfall, particularly in the non-monsoon season. The degree to which rainfed crops rely on moisture sourced from evaporation in upwind irrigated areas remains largely unknown. Using a combination of models and observations, we show that evaporation from upwind irrigated crops contributes 7% (mean) +/- 5% (spread) of the rainfall over rainfed areas annually, rising to 15 +/- 10% during the pre-monsoon months (averaged over the years 2000-2020). In the absence of this input, water stress experienced by rainfed crops can increase by 5%-10% during the crucial mid to late crop growth phases, potentially affecting yields. Our results reveal an unrecognized atmospheric link between irrigated and rainfed agriculture that is overlooked in current agricultural policies. Planning and managing these systems holistically can help strengthen regional food and water security under future climates.

High temperature (HT) over India has garnered increasing attention in recent years due to their substantial impact on human health and well-being. Given their growing significance, extensive efforts have been made to understand the driving mechanisms behind these events, as well as their projected changes under future climate scenarios using global climate models (GCMs). However, confidence in such projections depends critically on how well GCMs can replicate historical climate behaviour. In this study, we evaluate the performance of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in capturing the spatio-temporal characteristics of HT over India. Observational data reveal two distinct patterns: (i) an increasing trend in HT over southwestern India (SWI), and (ii) a pronounced east-west asymmetry over northern India, characterised by more frequent HT in the northwest and a decline in the east. The SWI HT pattern is closely linked to the El Ni & ntilde;o-Southern Oscillation, which is reasonably well represented by the CMIP6 models. In contrast, the models fail to reproduce the observed asymmetry in northern India, likely due to their inadequate simulation of the large-scale atmospheric response to the Atlantic Ni & ntilde;o. These findings underscore the importance of rigorous model evaluation before relying on CMIP6 projections to inform HT-related adaptation and policy strategies in a highly vulnerable region like India.

Floods and droughts are intensifying and increasingly co-occurring under hydroclimatic variability, yet vulnerability assessments typically consider these extremes in isolation, masking compounded risks. Here, we develop a unified framework to quantify district-scale vulnerability to floods, droughts, and their combined impacts across India. Central to the framework is the Multi-Hazard Vulnerability Index (MHVI), supported by a bivariate vulnerability classification that explicitly captures overlapping susceptibilities to opposing hydrological extremes. Applying the framework for 2001 and 2011 reveals a substantial escalation of vulnerability, with highly flood-vulnerable districts increasing from 16.5% to 21.4% and drought-vulnerable districts from 10.6% to 18.1%, alongside a growing concentration of multi-hazard hotspots in eastern and northern India. The results demonstrate pronounced spatial and temporal shifts in compound vulnerability, highlighting regions where development gains are increasingly exposed to multi-hazard risks. The scalable framework offers a robust basis for risk-informed adaptation planning, resource prioritization, and climate-resilient disaster risk reduction.

As climate and terrestrial ecosystems are closely coupled, climate variability can significantly impact the vegetation dynamics. Large-scale circulation patterns, such as El Niño-Southern Oscillation (ESNO), impact the spatial distribution of rainfall and temperature, and their extremes, which further affect vegetation productivity. ENSO is one of the primary drivers of Indian summer monsoon rainfall (ISMR), accounting for about 40% of its interannual variability. Some of India's most severe summer monsoon droughts are associated with the El Niño events. Pacific meridional mode (PMM), tropical Atlantic Niño and the surface temperature/pressure over the Middle East are also gaining attention as potential drivers of Indian summer monsoon rainfall and climate extremes over India. However, the control of ENSO and other teleconnections-induced climate variability on terrestrial ecosystem productivity is poorly understood, especially in terms of the spatial extent, strength, and underlying mechanisms. Here, we examine the relationship of Indian vegetation productivity with large-scale teleconnections such as ENSO and PMM. We use frequency decomposition and principal component analysis (PCA) to reveal the dominant timescales of variability in vegetation productivity and quantify its association with the large-scale features of climate variability. We find that while ENSO is the most significant driver of the vegetation productivity which causes ecological droughts over core monsoon region, PMM also has a significant control primarily on low frequency variability of Indian vegetation. Our findings quantify the primary climatic controls of variability in Indian vegetation and reveal PMM as a significant modulator of low frequency variability.

Recent studies suggests an increased frequency of Arabian Sea (AS) cyclones during post-monsoon season, causing widespread socio-economic damages. Thus, for the improved prediction of these events, understanding the physical mechanism is important which immensely helps in disaster risk management on the western coast of India. Here, using long-term observational data and climate model perturbation experiments, we show that the post-monsoon AS cyclones are influenced by the variability of the North Atlantic Sea Surface Temperature (NASST). NASST significantly reduces vertical wind shear forming a favourable condition for the cyclones to form over the AS. The formation of conditions can be accounted towards alteration in the Walker Circulation. Further, using the Coupled Model Intercomparison Project Phase 6 (CMIP6) models, we show that the conditions in the NASST driving these responses are exacerbated by the greenhouse gas emission, demonstrating that the contribution of anthropogenic influences to NASST variability in recent times outweighs natural variability. If emissions are not contained with proper mitigation measures, a further increase in NASST may also increase the post-monsoon AS cyclonic activities. Link between the North Atlantic Ocean (NASST) and the cyclogenesis in Arabian Sea. The link is through the alteration in the Atlantic Walker circulation. NASST driving these responses are exacerbated by the anthropogenic influences.

Tropical cyclone-related losses are projected to increase globally due to climate change and socio-economic factors, with storm surges posing a significant threat to coastal regions. Enhanced preparedness among coastal populations is essential to reduce the impact of this trend. This study evaluates storm surge hazards and risks using a multi-attribute decision-making method and develops risk maps based on empirical data. The integration of hazard, vulnerability, and exposure indices highlights the eastern coast (Bay of Bengal) as the region with the highest present risk. Risk levels are comparatively lower along the Arabian Sea and Indian Ocean coasts, but they still pose substantial threats, particularly in urbanized and low-lying areas. Additionally, by offering data-driven insights into risk management, the analysis facilitates the development of adaptable infrastructure and land-use planning for coastal resilience. Future research will focus on refining the hazard component to enhance the accuracy of risk assessments.

Vegetation productivity in India varies at intraseasonal to interannual time scales, influenced by meteorological factors sensitive to large-scale climate teleconnections. While the impact of global climate variability on Indian monsoon and its extremes is well known, their effects on Indian vegetation productivity are relatively less understood. This study addresses this gap by decomposing dominant modes of spatio-temporal variability of gross primary productivity (GPP) over India and examining their dependence on climate teleconnections. We found that El-Ni & ntilde;o Southern Oscillation (ENSO) and Pacific Meridional Mode (PMM) significantly impact GPP, especially in western and southern peninsular India during the monsoon and post-monsoon seasons (correlation coefficient = similar to 0.5). However, there is an east-west asymmetry in the PMM-GPP correlation. The western region and southern peninsula are negatively correlated, while northeast India positively correlates with PMM. Using wavelet decomposition, we show that more than half of temporal variability in the GPP comprises low-frequency components. These low-frequency signals primarily drive the relationship between GPP and climate teleconnections. Next, we identify the dominant spatial modes of low-frequency signals of GPP. We tested the predictability of the principal components of GPP using teleconnections and hydrometeorological variables. While most of the predictive skill of GPP comes from its past (memory up to 5 months, R 2 score of up to 0.5), adding teleconnection indices as predictors improves the prediction skill at lead times (with an increase of 30%-50% in R 2 values, and up to 10%-15% reduction in RMSE). Our results underscore the utility of using hydrometeorological and distant climate teleconnection in GPP prediction for longer lead times.

The Arctic is experiencing heightened precipitation, affected by aerosols impacting rainfall and snowfall. However, sparse aerosol observations in the central Arctic cryosphere contribute to uncertainties in simulating aerosol-precipitation two-way interaction. This study examines aerosol-precipitation co-variation in various climate models during the Arctic spring and summer seasons from 2003 to 2011, leveraging satellite-based aerosol data and various CMIP6 climate models. Findings reveal significant spatio-temporal biases between models and observations. Snowfall dominance occurs in models where total AOD surpasses the observation by 121% (57–186%, confidence interval), intensifying simulated snowfall by two times compared to rainfall during summer. Consequently, climate models tend to underestimate central Arctic rainfall to the total precipitation ratio, suggesting a positive bias towards snowfall dominance. This highlights the importance of constraining total AOD and associated aerosol schemes in climate models using satellite measurements, which potentially could lead to a substantial reduction in snowfall contribution to the total precipitation ratio in the central Arctic, contrary to current multi-model simulations across various spatiotemporal scales.

The western United States (U.S.) has been experiencing more severe wildfires, in part due to climate change, but the underlying synoptic patterns and their modulation in driving fire weather is unclear. Here we investigated the relationship between weather regimes (WRs) and fire weather indices, specifically vapor pressure deficit (VPD) and the Canadian Forest Fire Weather Index. By identifying five singular WRs using k-means clustering, we found that a particular regime (WR-2), one characterized by a distinct tripolar wave train pattern over the continental U.S., has exhibited an increased frequency since 1980. The ascribed WR-2 regime was found to be mainly responsible for rising trends in the fire weather indices, especially VPD. Further, the average fire indices of the WR-2 regime played a more important role than the frequency in shaping the rising trends in the fire weather indices. The increased frequency of the WR-2 WR was mainly attributed to anthropogenic forcing and, the year-to-year variation of the frequency was associated with sea surface temperature anomalies over the subtropical eastern Pacific. Human-induced climate change might have furthered the exacerbation of wildfire danger in the western U.S. by modulating the behaviors of WRs and fire weather indices.

This study examines the dynamics of the urban heat island (UHI) effect by conducting a comparative analysis of air temperature hysteresis patterns in Paris and Madrid, two major European cities with distinct climatic and urban characteristics. Utilizing high-resolution modelled air temperature data aggregated at a fine temporal resolution of three-hour intervals from 2008 to 2017, we investigate how diurnal and seasonal hysteresis loops reveal both unique and universal aspects of UHI variability. Paris, located in a temperate oceanic climate, and Madrid, situated in a cold semi-arid zone, display pronounced differences in UHI intensity, seasonal distribution, and diurnal patterns. Despite these contrasts, both cities exhibit remarkably similar hysteresis loop directions and slopes, suggesting that time-dependent mechanisms such as solar radiation and heat storage fundamentally govern air temperature UHI across diverse urban contexts. Our findings underscore the importance of considering both local climate and universal physical processes in developing targeted, climate-resilient urban strategies. The results pave the way for group-based interventions and classification of cities by hysteresis patterns to inform urban planning and heat mitigation efforts.

Tropical cyclones (TCs) in the North Indian Ocean (NIO) constitute only 6% of global TCs but cause over 80% of cyclone-related fatalities. Conventional wind speed-based assessments often underestimate the compound nature of TC hazards. We develop a multivariate framework integrating wind speed and precipitation to characterize TC compound hazards in India (1951–2020). Using multivariate dependence modeling, we estimate joint return periods, conduct district-level exposure characterization, and assess non-stationarity. Advanced metrics, including the Shannon surprise, various statistical distance metrics, and fraction attributable risk, reveal regions with heightened susceptibility and the climate change influence on cyclone risks, especially notable among districts of Odisha, Tamil Nadu, and Andhra Pradesh. This methodologically simple and computationally efficient framework, based on a quasi-Lagrangian perspective, while demonstrated for India, offers a scalable methodology for assessing compound TC hazards to cyclone-prone regions globally. It supports targeted adaptation strategies, contributing to the broader goals of sustainable development and climate resilience.

The eastern European (EE) region has experienced record-breaking heatwave events in recent years, and such events are expected to increase in future with global warming. Early warning systems are an important step towards mitigating their impacts. Here we seek to further clarify the effect of Atlantic Meridional Mode (AMM) on the EE region temperature variability. Using observations and climate model experiments, we show a significant association between the AMM and temperature variability across the region. The positive phase of AMM leads to a significant increase in EE temperature of 0.9 C, p-value < 0.1, for a one standard deviation AMM anomaly, and vice-versa. The mechanism through which the AMM can modulate the EE temperature arises through a persistent planetary-scale Rossby wave which causes an anomalous anticyclone circulation leading to a positive temperature anomaly. This relationship, along with the mutually exclusive and independent large-scale climatic modes such as the El Nino-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO), have important implications for improving the prediction of EE heatwaves.

Historically, the precipitation trend over the past few decades in the Contiguous United States (CONUS) exhibits a “Dry‐West Wet‐East” pattern; this is manifested by recent droughts/floods in the western/eastern US. However, it remains elusive what atmospheric phenomenon has potentially driven such a remarkable, and impactful precipitation pattern. Here we found that a coupled climate mode—the Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by using Atmospheric Model Intercomparison Project simulations available from the Coupled Model Intercomparison Project Phase 6.

Anthropogenic global warming has led to widespread increase in the heatwave intensity, duration, and frequency (HIDF) of events across the globe. Although the three characteristics of heatwaves are closely interconnected, they are often studied separately, especially over the Indian subcontinent. Here, we assess the HIDF over India during the period 1961-2023, and show that there exists a systematic East-West asymmetry in their characteristics over northern India. Specifically, we show that a substantial increase (decrease) in HIDF exists over major cities in the western (eastern) parts of North India. For example, Ahmedabad (a city in the western part) shows a 67.5% increase in the likelihood of four-day heatwaves in the recent decades (1991-2023) compared to the reference period 1961-1990. On the other hand, in the city of Patna (in the eastern part), we notice a 78% decrease in the likelihood of four-day heatwaves in the recent decades. Furthermore, we show that the East-West asymmetry in the HIDF is caused by a major climatic mode, i.e. the Atlantic Ni & ntilde;o. Overall, our study provides a first insight into the contrasting characteristics of heatwaves over Northern India and their potential drivers. Such information is vital for the design of regionally appropriate adaptation strategies across India.

Under global warming, heatwaves over India are projected to increase unequivocally. Thus, understanding the causal mechanisms responsible for heatwaves adds immense value to the country’s climate change mitigation and adaptation strategies. Although several studies have attempted to understand the physical mechanisms responsible for such events, efforts focusing on South-West India (SWI) are lacking. Here, using long-term observational data (1951–2020) and climate model simulations, we show that the heatwaves over SWI are influenced by major climatic modes, including El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and the Indian Ocean Dipole (IOD). The mechanism through which these climatic modes affect the SWI heatwaves is either by weakening of Pacific Walker circulation (ENSO and PDO) or by strengthening the Indian Ocean Hadley circulation (IOD). Further, we found that amongst these climatic modes, PDO explains the majority of the heatwave variability followed by IOD. However, post-1979, the scenario has changed remarkably with ENSO gaining prominence, signifying the strengthening of the relationship between ENSO and SWI heatwaves in the recent past. Overall, our study provides a first insight into the drivers and mechanism of the SWI heatwaves, whose value to designing heatwave adaptation strategies over the ecologically sensitive region can hardly be overemphasised.

Calibration of hydrological models for watersheds is critical considering the hydrological processes involved. The Soil and Water Assessment Tool (SWAT) is one such popular model and requires proper calibration, without which models have difficulty in proper simulation of runoff. The present study aims to utilize multi-objective calibration framework using Non-Dominated Genetic Algorithm- II (NSGA-II) and SWAT-Calibration Uncertainty Procedures (SWATCUP) for calibration. The study is conducted on Musi river basin located in India (10,000 Sq km) for seven years from 2013-2016. It includes an initial warm-up period of three years, the calibration period from 2015-2016, and validation period from 2014-2015. NSGA-II aims to optimize the multiple objective functions i.e. Nash Sutcliffe Efficiency (NSE) and Percentage Bias (PBias). The Monthly simulations results are expressed in terms of statistical parameters NSE, R-2 and PBias for calibration and validation period. The results indicate satisfactory performance. Further, NSGA-II results are compared with SWATCUP (Sequential Uncertainty Fitting ver.2 (SUFI-2). We find NSGA-II performance is better than SWATCUP. The sensitive analysis indicates that CN2, GW_DELAY, GW_REVAP, ALPHA_BF, RCHRG_DP, and CH_K2 are very sensitive whereas SURLAG, ESCO, SLSUBBS, HRU_SLP are observed to be least sensitive.

The fast depletion of soil moisture in the top soil layers characterizes flash drought events. Due to their rapid onset and intensification, flash droughts severely impact ecosystem productivity. Thus understanding their initialization mechanisms is essential for improving the skill of drought forecasting systems. Here, we examine the role of antecedent meteorological conditions that lead to flash droughts across Europe over the last 70 years (1950–2019) using ERA5 dataset. We find two major flash-drought types based on a sequence of development of antecedent hydro-meteorological conditions. The first type is characterized by a joint occurrence of two mechanisms, a decline of precipitation in conjunction with an increase of the evaporative demand, both occurring before the onset of a flash drought event. The second type, on the contrary, is characterized by high precipitation preceding the event’s start, followed by a sudden precipitation deficit combined with an increase in evaporative demand at the onset of the drought. Both drought types showed increased occurrence and higher spatial coverage over the last 70 years; the second drought type has increased at a much faster rate compared to the first one specifically, over Central Europe and the Mediterranean region. Overall our study highlights the differences between the two types of flash droughts, related to varying antecedent meteorological conditions, and their changes under recent climate warming.