Global hotspot regions where soil moisture (SM) constrains temperature changes are expected to migrate and change in intensity under climate change, impacting hydroclimatic events; however, the nature of these changes is still uncertain. Using multiple model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we assessed potential future changes in the coupling between boreal summer SM and near-surface mean air temperature (T) across the globe under four Shared Socioeconomic Pathways (SSPs, 2015–2100). We find weakening SM impacts on T (SM-T coupling) in semi-arid, low-latitude regions with increasing emission scenarios due to reduced sensitivity of evaporation to SM. However, our results showed intensifying SM-T coupling primarily over humid regions with increasing precipitation yet decreasing SM due to increasing evaporation. We demonstrate that these changes could be linked to the poleward expansion of the Hadley cells and water-limiting conditions, shifting SM controls on partitioning the surface net radiation and subsequently on T under global warming. These results suggest a higher likelihood of extreme hydroclimatic events, such as heatwaves in higher latitudes associated with the SM–T coupling, which could impact food and water security.

Flood risk assessment is crucial for effective disaster risk management and community resilience. However, the current research lacks strength in identifying high-risk areas, implementing flood early warning systems, prioritising risk reduction measures, and allocating resources for emergency response planning and management. This study aims to assess flood hazard in Mirzadhare, Charsadda a highly flood-prone area in Khyber Pakhtunkhwa province of Pakistan. The study used an integrated approach by employing geographical information system (GIS) and multi-criteria decision analysis (MCDA) techniques. Further, the study used multiple datasets, including rainfall, stream density, and village points to map out flood susceptibility in the study region. Data was collected from field surveys, questionnaires, and interviews, allowing for a detailed analysis of flood hazards. Selecting average precipitation, peak river flow, and historical flood frequency as indicators, the weights of the three are 0.4, 0.3, and 0.3 respectively, an indicator system for predicting flood disasters was constructed. The results categorised the study area into four hazard zones: very high, high, medium, and low, based on their susceptibility to flood hazards. The study findings reveal that more than 65% of the area, including agricultural land with other livelihood settlements, is at a very high risk of flood hazard. Over 50% of the population lives in floodplains and faces an extremely high risk of future flood events. The precision of the results may have been affected by the accuracy and completeness of the data sources utilised, such as historical flood records, precipitation data, stream network data, and stream density. This combination of methods enabled the creation of accurate, data-driven flood risk maps. The hazard map of the area serves as a valuable tool for decision-making, resource allocation, and the development of flood risk management strategies. Based on the study findings, regular updates and continuous monitoring are recommended to ensure the accuracy and relevance of the flood hazard information over time.

Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40-0.48%, 0.83-0.93%, 1.29-1.45% and 1.70-1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8-31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0-13.2% or 0.12-0.37°C) and in total evaporation (4.8-14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors.

Anthropogenic climate change has amplified human thermal discomfort in urban environments. Despite the considerable risks posed to public health, there is a lack of comprehensive research, evaluating the spatiotemporal changes in human thermal discomfort and its characteristics in hot-hyper arid regions, such as the Arabian Peninsula (AP). The current study analyzes spatiotemporal changes in human thermal discomfort categories and their characteristics in AP, using the newly developed high-resolution gridded ERA5-HEAT (Human thErmAl comforT) dataset for the period 1979–2022. In addition, the study assesses the interplay between the Universal Thermal Climate Index (UTCI) and El Niño-Southern Oscillation (ENSO) indices for the study period. The results reveal a significant increase in human thermal discomfort and its characteristics, with higher spatial variability in the AP region. The major urban centers in the southwestern, central, and southeastern parts of AP have experienced significant increases in human thermal discomfort (0.4–0.8 °C), with higher frequency and intensity of thermal stress during the study period. The temporal distribution demonstrates a linear increase in UTCI indices and their frequencies and intensities, particularly from 1998 onward, signifying a transition towards a hotter climate characterized by frequent, intense, and prolonged heat stress conditions. Moreover, the UTCI and ENSO indices exhibit a dipole pattern of correlation with a positive (negative) pattern in the southwestern (eastern parts) of AP. The study’s findings suggest that policymakers and urban planners need to prioritize public health and well-being in AP’s urban areas, especially for vulnerable groups, by implementing climate change adaptation and mitigation strategies, and carefully designing future cities to mitigate the effects of heat stress.

The 2-m temperature data is a significant indicator for studying the weather extremes and the exchange of water and energy fluxes between the surface and atmosphere. This study compared three reanalysis datasets, i.e., ERA5, ERA5-Land, and MERRA-2, with observations from in-situ sources from 1990 to 2022 using various statistical error metrics and extreme temperature indices over the Arabian Peninsula (AP) region. We selected these reanalysis datasets due to the continuous improvements and higher spatiotemporal output better capturing the temperature variability. The spatiotemporal climatology shows lower temperatures in winter (<15 degrees C) and maximum in summer (>35 degrees C); however, the reanalysis data show more deviations in temperature during the cold season than in the warm season. The reanalysis data underestimated the frequency of the cold (<10 degrees C) and hot (>30 degrees C) days across the four regions, except ERA5-Land, which closely followed observed data. The strength of the correlation shows better performance (>0.90) in the cold extremes (cold nights and cold days) frequency than the hot extremes. On an interannual scale, reanalysis products exhibit strong correlations (>0.90) with in-situ data across most regions, particularly in winter and autumn, moderate in spring, and weaker in summer. The reanalysis data shows negative biases in the inland regions and positive biases in the coastal areas with consistent root mean square differences (RMSD) spatiotemporally. The differences in performance are due to the topography and poor representation of the energy fluxes, especially in MERRA-2 as well as missing data in observations. This study recommends ERA5-Land as the first choice for extreme weather simulations in the region, followed by MERRA-2 and ERA5 on the same scale, but proper attention is needed when using reanalysis data for cold and hot extremes.

An integrated approach using geophysical and geospatial techniques was employed to model the surface and subsurface water-bearing strata and assess aquifer vulnerability in the Sehnsa town, Kotli district, State of Azad Kashmir, Pakistan. The inadequate scientific studies in the hilly terrain with such complex geological conditions has led to the failure of the boreholes for groundwater extraction. For the evaluation of groundwater potential and subsurface lithology, 30 vertical electrical soundings (VES) stations utilizing the Schlumberger electrode configuration were completed, modeled and analyzed spatially. Numerous geoelectrical parameters like true resistivity, thickness of subsurface layers and Dar-Zarrouk parameters were evaluated. The subsurface lithology delineated comprised topsoil, clayey sand, sandstone, and boulder clays which closely resemble to the borehole lithologs available in the study area. The inversion model confirms the presence of patches of high-resistivity sandstone in the southwestern part of the study area with the maximum thickness of the aquifer up to 140 m. Most aquifers were classified as unconfined with Q-type resistivity curves. The protective overburden capacity of the aquifers is rated as poor at VES 1, 3-5, 8, 10-16, 18, 19, 22-25, 27 and 30 whereas the moderate category was found at VES 2, 9 and 20 and excellent at VES 7 and 28, respectively. Therefore, the VES stations with poor and moderate ratings of overburden protective capacity are vulnerable for surface contaminants. The aquifer recharge was associated with rainfall and partly from the Poonch River. The effective integration of geophysical and geospatial techniques in this study provides sufficient information about the regional water resources and gives a preliminary model that can facilitate efficient water resource management in the area. These approaches can be successfully applied to diverse geographical and hydrogeological sites due to their versatility and reliability.

Over the last two decades, urban floods and their impacts have been on the rise worldwide, owing to both climatic changes and human activities. The present study examines different at-risk elements, such as residential, commercial, and critical facilities, to evaluate their multi-components of vulnerability to urban floods in Peshawar, Pakistan. Based on the impacts of urban floods, the weightage of each component of the vulnerability for the selected elements at risk is defined. This study presents and uses the modified Fisher's ideal quantity index to combine the different vulnerability components into a single value. Additionally, the Patnaik and Narayan vulnerability index is employed to generalize sector-wise vulnerabilities across the study area. The results show that the old physical infrastructure of commercial and manufacturing units in the Kohati Gate area is highly vulnerable to urban floods, while the residential units are the least susceptible due to their distanced location from the drainage system. In Hayatabad, encroachments along the torrent's sides, affecting housing and educational institutions, contributed to increased vulnerability to urban floods, despite their relatively lower physical vulnerability. The study provides a new platform for understanding the multi-components of vulnerability to urban floods and tackling the challenges posed by urban floods effectively.

Floods are among the most destructive natural disasters, causing extensive damage to human lives, property, and the environment. Pakistan is susceptible to natural calamities, such as floods, resulting in millions of people being impacted yearly. It has been demonstrated that flood severity is rising and may continue to escalate in the coming years because of climate change-induced changes in monsoon precipitation in the country. Given the country’s exposure to flooding, it is essential to assess the vulnerability to floods to prepare for and mitigate their impact in Pakistan. This study provides a new conceptual framework for assessing flood risk and vulnerability in Charsadda, a flood-prone district in Pakistan. It evaluates the vulnerability of settlements to floods based on four indicators: population density, the average gross domestic product (GDP) of land, the distance between settlements and rivers, and land use and cover (LULC). The analytical hierarchy process (AHP) technique was integrated with the geographical information system (GIS) to assess the level of vulnerability to floods in the study area. The results reveal a higher degree of vulnerability to floods in the region. The spatial pattern of vulnerable areas reveals a significant connection between high-risk flood areas and densely populated areas during different flood seasons. The results further reveal that more than 60% of the area is arable land and is highly susceptible to flood. The population and their land-use setup show high and extremely high values of vulnerability in the normalized threshold of 0.3–0.4, respectively. The study provides an in-depth and comprehensive analysis of the chosen indicators, evaluation methods, and results, making this a valuable contribution to the field of flood vulnerability assessment. The findings of this study also include thematic maps and related information to the stakeholders for effective vulnerability management in the study area.

This study comprehensively evaluates monsoon variability and its large-scale drivers in Pakistan, a country often affected by extreme monsoon events leading to destructive flooding. Utilizing the Monotonic Mann-Kendall and Sen's Slope test, we analyzed trends in seven indicators of extreme precipitation events (EPEs) from 1961 to 2017 across Pakistan and its homogeneous climatic regions. The wavelet coherence method further allowed us to probe the connection between EPEs and key climate indices such as the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), El Nino-Southern o-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Indian summer monsoon (ISM), and Southern Oscillation Index (SOI) in the time- frequency domain. Our findings indicate a significant increase in all EPEs across Pakistan, particularly in the monsoon region (R-II) from 1961 to 2017, with a noticeable decline post-1980s. The findings revealed a significant increase in EPEs in the whole country from 1961 to 1989 to 1990-2017, with a dominant change in the monsoon region during 1990-2017. In terms of climate indices combinations, IOD + ISM showed the highest coherences (>0.65), while ENSO+IOD +IOD + ISM and ENSO+IOD +IOD + PDO displayed coherences >0.81 and > 0.79, respectively, across the entire country. The multi-linear regression (MLR) model analysis drivers indicate that IOD and ENSO are the leading drivers of changes in monsoon EPEs in the whole country, whereas the core monsoon region of Pakistan also showed a strong influence from AMO and NAO in tandem with ENSO and IOD. The overall increase in EPEs over Pakistan are mainly associated with the indices from the Pacific Ocean, especially IOD, ENSO and PDO and with their various combinations. Our study reveals the variations in EPEs across Pakistan, yielding insights that could inform water resource management strategies and disaster prevention measures.

Unveiling the complexities of Earth's climate system demands a profound understanding of the intricate interplay between soil moisture (SM) and 2m air temperature anomalies (T). This study employs cutting-edge methodologies, such as nonlinear Random Forest Granger Causality (nRFGC), Copula nonlinear Granger Causality (CnGC), nonlinear Kernel Granger Causality (nKGC), and the traditional linear Granger Causality (GC), to unravel the complex causal relationship between SM and T. Through extensive experimentation on both hypothetical and real-world datasets from diverse sources, the research underscores the superior efficacy of nonlinear methodologies—especially nonlinear KGC—in identifying elevated Granger causal signals across spatial and temporal dimensions. Notably, these findings align consistently with both hypothetical and traditional hydrological models, underscoring the limitations of linear methodologies when grappling with nonlinear causation. The study provides clarity on discerning the nonlinear causal link between SM and T, emphasizing the imperative to transcend conventional linear methods when unraveling the intricate causal complexities within Earth's climate system. Significantly, Nonlinear Granger Causality (nGC) emerges as a potent tool capable of unveiling causal structures without succumbing to overfitting issues. Consequently, this research imparts insightful revelations about the non-linear dynamics inherent in SM-T interactions across different seasonal levels. Our study clarifies and makes it clearer to identify the nonlinear causal relationship between SM and T, emphasizing the need to go beyond conventional methodologies, asserting that these methodologies fall short in comprehending the nuanced causal interactions between land and atmosphere. By urging a shift, the study contends that embracing non-linear methodologies is essential not only for enhancing predictions but also for gaining a more profound understanding of Earth's climate system.

Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.

As a low-income country, Pakistan is particularly vulnerable to various natural and human-induced disasters, which have significant consequences for both the environment and human life. A substantial share of the provincial budget is allocated to disaster response and recovery efforts. Prioritizing investment in disaster risk reduction (DRR) is essential to protect lives and assets. Although there is a complex relationship between investments made before and after disasters and their effectiveness in DRR, this dynamic remains insufficiently understood in Pakistan’s Khyber Pakhtunkhwa (KP) province. The current study was designed to analyze the developmental budgeting for DRR in the financial years 2000–2022 in KP province. The Sen’s Slope Estimator and modified Mann-Kendall tests were used to determine the significance trend, while the correlation coefficient test was used to find the correlation between investment in DRR and disaster-induced damages such as deaths, injuries and houses damaged. The study findings reveal that the occurrence of disasters influences post-disaster spending in the KP province, with a significant negative correlation between expenditure and disaster-related damages, implying that increased DRR investment has significantly reduced the consequences of disasters. To minimize vulnerability to future disasters, the province should integrate risk-sensitive planning across all sectoral departments at local, district, and provincial levels, guided by a risk-informed development approach. This proactive strategy would embed disaster resilience within developmental activities, ensuring that each sector aligns with principles of risk reduction and sustainable growth.

Understanding the effects of thermal and water stress on maize yield in the context of climate change is crucial to ensure food security in China. However, very few studies looked into the combined effects of heat and water stress on maize yield in China. Here, we utilized historical reanalysis data from ERA5 and four future shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) of the Coupled Model Intercomparison Project 6 (CMIP6) models to predict the maize yield. We used the linear mixed-effects model to quantify the grid cell sensitivity of vapor pressure deficit (VPD) and root-zone soil moisture to maize yield in China during 2010–2016. The results infer that VPD and root-zone soil moisture are excellent representatives of heat and moisture stress. Maize yield is beneficial only when the atmospheric moisture demand and soil moisture are in relative balance. Based on the historical results’ polynomial function for VPD and soil moisture, we predict the maize yield response to soil moisture and VPD in the four SSPs. The results show that considering soil moisture in the future the projected yield estimates reduce the overestimated yield loss by half compared to considering only atmospheric moisture requirements. Maize yield will decrease under representative SSPs due to an increase in temperature (1.5, 2.0, 2.5, 3.0, 3.5, and 4.0°C). This study suggests that both atmospheric moisture demand and supply need to be considered when analyzing the specific influence of climate change on crop yield to secure and assure global food supplies.

Soil moisture as a key variable of land processes greatly influences the weather and climate. This study investigates the observed linkage between Tibetan Plateau (TP) spring soil moisture and summer precipitation and floods in the Yangtze River basin during 1988-2008 using satellite and in-situ observations. A significant (p = 1%) proportion of interannual variations of summer precipitation (about 25%) in the Yangtze River basin can be attributed to spring TP soil moisture anomalies which show a dipole pattern. When spring soil moisture anomalies are positive (negative) over eastern (western) TP, there is more summer precipitation and consequently river discharge in the Yangtze River basin, or vice versa. The possible mechanisms can be explained from the perspectives of surface energy balance and atmospheric thermodynamics. More (less) spring soil moisture over the eastern (western) plateau enhances summer diabatic heating, which may be related to soil moisture memory. The enhanced summer diabatic heating stimulates vigorous ascending motions over TP, which diverge in the upper troposphere (200 hPa) and descend over the western Pacific. This is conducive to the enhancement and mutual proximity of the South Asian High (SAH) and the Western Pacific Subtropical High (WPSH). As a result, the warm and humid air flows from the Bay of Bengal and the western Pacific and the cold and dry air flows from the boreal continent converge in the Yangtze River basin, causing excessive summer precipitation. Therefore, TP spring soil moisture can be considered a seasonal predictor of summer precipitation and possible subsequent floods in the Yangtze River basin.

These datasets are results from merging three FengYun passive microwave soil moisture observations at a 15kmx15km spatial resolution from 2011 to 2020 with continuous extension as data becomes available. Here, we rely on a merging technique that minimizes mean square error (MSE) using the signal-to-noise ratio (SNRopt) of the input parent products to first merge subdaily soil moisture products into dail averages (FY3_Merged_*). From these, these are gap-filled using a Data INterpolating Convolutional Auto-Encoder, DINCAE (FY3_Reoconstructed_*). The advantage of this method is that it comes with error variances(FY3_ErVar_*) for each pixel and time step which are useful for sevral applications.

This study assessed spatiotemporal trends in daily monsoon precipitation extremes at seasonal and sub-seasonal scales (June, July, August, and September) and their links with atmospheric circulations over Pakistan. The study used observed precipitation data from fifty in-situ stations and reanalysis products from the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) during 1981–2018. A suite of seven extreme precipitation indices and non-parametric statistical techniques were used to infer trends in the frequency and intensity of extreme precipitation indices. An increase in frequency and intensity of overall extreme indices was evident, with a maximum tendency in the country’s northwestern (z-score=>2.5), central, and eastern (z-score > 4) monsoon-dominant parts. The northern and southwestern parts of the country exhibited a slight decrease (z-score <–2) in frequency and intensity. The Sen’s Slope estimator (SSE) shows an increase in western parts (0.20 days) indicating a shift in the maxima of the monsoon precipitation. The regional precipitation shows an increase in wet days (R1 mm) with higher values of mMK (3.71) and SSE (0.3) in region 2 Similar results of moderate regional increase are evident for extreme indices except regions 1 and 3. The extreme 1-day maximum precipitation increased in region 3 (mMK: 1.39, SSE: 2.32). The extremely wet days (R99p TOT) precipitation has a moderate increase in all regions with a decrease in region 1. The temporal mutations showed dynamic changes, clearly reflecting the country’s historical extreme events. The frequency and intensity of precipitation extremes negatively correlated with the altitude (R = −0.00039). The probability density function (PDF) showed a significant increase in the density during June and September with a probabilistic positive shift during July and August. The intensified mid-latitude westerlies and subtropical zonal easterlies teleconnections, strengthening of the monsoon trough, and land-ocean thermal contrast are the potential drivers of the increasing trend in precipitation extremes. The current study could serve as a benchmark for future researchers and policymakers to devise effective mitigation strategies for sustainable development.

As an internal forcing of the earth climate system, soil moisture (SM) significantly influences the water and energy cycle by controlling evapotranspiration and terrestrial solar energy. The current study used observed precipitation, remotely sensed SM, and reanalysis of atmosphere and land parameters to assess the East Asian Monsoon (EAM) precipitation variability due to meridional SM oscillations across China. A generalized linear method, namely coupled manifold technique (CMT) for assessing the reciprocal forcing between two climate fields and numerical simulations are applied to SM and EAM precipitation. We find that the EAM precipitation interannual variability between north and south China significantly correlates with SM meridional oscillation. The CMT results further showed that SM forcing has a significant (99% confidence) influence on the EAM precipitation explaining about 0.40 of the variance ratio in north and south China. The EAM and SM composite analysis show that the wetter (drier) north (south) oscillates the EAM precipitation over the north (south) of China and vice versa due to SM thermal controls. We then used control and sensitivity simulations with SM observations to further validate the findings implying that SM can potentially improve the interannual EAM forecast skills. The model results show that a wetter (drier) north (south) results in negative (positive) sensible heat (latent heat) anomalies that impact the boundary layer and propagate to change the meridional atmospheric heating profile. When positive (negative) SM anomalies exist over northern (southern) China, the zonal easterlies and extratropical westerlies move to north China causing above-normal precipitation that descends into southern China, suppressing subtropical westerlies and precipitation in southern China. On the contrary, a dry (wet) north (south) favors intensified subtropical westerlies and precipitation in southern China. The findings have dire implications for the water and energy cycle of the region in the projected wetting and drying patterns of the north (south).

Extreme precipitation events are one of the most dangerous hydrometeorological disasters, often resulting in significant human and socio-economic losses worldwide. It is therefore important to use current global climate models to project future changes in precipitation extremes. The present study aims to assess the future changes in precipitation extremes over South Asia from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The results were derived using the modified Mann-Kendall test, Sen’s slope estimator, student’s t-test, and probability density function approach. Eight extreme precipitation indices were assessed, including wet days (RR1mm), heavy precipitation days (RR10mm), very heavy precipitation days (RR20mm), severe precipitation days (RR50mm), consecutive wet days (CWD), consecutive dry days (CDD), maximum 5-day precipitation amount (RX5day), and simple daily intensity index (SDII). The future changes were estimated in two time periods for the 21st century (i.e., near future (NF; 2021–2060) and far future (FF; 2061–2100)) under two Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5 and SSP5-8.5). The results suggest increases in the frequency and intensity of extreme precipitation indices under the SSP5-8.5 scenario towards the end of the 21st century (2061–2100). Moreover, from the results of multimodel ensemble means (MMEMs), extreme precipitation indices of RR1mm, RR10mm, RR20mm, CWD, and SDII demonstrate remarkable increases in the FF period under the SSP5-8.5 scenario. The spatial distribution of extreme precipitation indices shows intensification over the eastern part of South Asia compared to the western part. The probability density function of extreme precipitation indices suggests a frequent (intense) occurrence of precipitation extremes in the FF period under the SSP5-8.5 scenario, with values up to 35.00 d for RR1mm and 25.00–35.00 d for CWD. The potential impacts of heavy precipitation can pose serious challenges to the study area regarding flooding, soil erosion, water resource management, food security, and agriculture development.

The ability of soil moisture (SM) to affect precipitation (P) is a vital part of the water-energy cycles. Accurately quantifying this coupling enhances the ability to predict hydroclimatic extremes like floods and droughts. In this study, the ability of soil moisture to affect precipitation (SM-P) is characterized by two parts: the influence of soil moisture on evapotranspiration (SM-ET), and the influence of evapotranspiration on precipitation (ET-P). We determined localized ET-P by incorporating the coupling between latent heat flux (LH) and LCL height, to optimize the estimation of the SM-P. This approach links SM more closely to P by considering the influence of surface fluxes. The results indicate that CMIP6 models exhibited the anticipated hotspot patterns for the three coupling metrics in transition regions. However, we observed that climate models generally exhibit weaker SM-P coupling compared to reanalysis models. Both SM-ET and SM-P showcase higher values wherein wet climate regions during dry years, and the converse occurs in dry regions. Due to sensitivity to climate change, the ET-P exhibits a more pronounced upward trend in the future. This study helps understand P’s response to SM shifts in climate models, crucial for predicting hydrological extremes and coupled global warming impact.

Transpiration from vegetation accounts for about two thirds of land evapotranspiration (ET), and exerts important effects on of global water, energy, and carbon cycles. Resistance-based ET partitioning models using remote sensing data are one of the main methods to estimate global land transpiration, overcoming the limitation by the sparse distribution and short observation periods of site-level measurements. However, the uncertainties of estimated transpiration for these models mainly come from the resistance parameterization based on specific empirical parameters across different plant functional types (PFT). A model based on eco-evolutionary optimization (P model) has recently been proposed to simulate stomatal conductance without the need of calibrated parameters. Here, we calculated global long-term (1982-2018) monthly transpiration with the Penman-Monteith (PM) equation using canopy conductance estimated by the P model (PM-P) and Ball-Berry-Leuning model (PMBBL). Using the observations of SAPFLUXNET and FLUXNET sites as reference, the performance of PM-P was comparable with that of PM-BBL and Global Land Evaporation Amsterdam model (GLEAM). Multi-year mean and trends in growing season transpiration estimated by GLEAM and the PM-P model revealed a similar spatial distribution globally. Both GLEAM and the PM-P model showed a widespread increasing trend of growing season transpiration over 72.06%similar to 80.38% of global land, especially for some main greening hotspots with >3.0 mm/ year. The good performance of the P model indicated that it could avoid the uncertainties emerging from the resistance parameterization with too many empirical parameters and had the potential to accurately estimate global transpiration.