Loss and recovery of terrestrial carbon sinks induced by 2020 extreme precipitation in the Yangtze River Valley

Spatial concurrence risk of extreme precipitations in Southeast Asia under climate change using temporally dynamic complex networks

Effects of cloud microphysics on the simulation of extreme precipitation over the Tibetan Plateau region

Future projections of extreme precipitation over Indonesia's new capital under climate change scenario using CORDEX-SEA regional climate models

When less travel means more carbon: How rainfall-induced shifts from public transit to cars increase urban transport emissions.

Machine learning prediction of summer extreme precipitation days in the middle and lower Yangtze River with SHAP explanation

Impacts of the urban heat island on the convective initiation and propagation of an extreme precipitation event in the coastal megacity of Shanghai

25-years study (2000–2024) of extreme precipitation following heatwaves in the Middle East: Regional patterns, trends, and atmospheric drivers

Abrupt decline and subsequent recovery of extreme precipitation associated with Atmospheric Rivers in the Southeastern Tibetan Plateau

Classification of extreme precipitation events in Pakistan and their multiscale interaction mechanisms

Abstract The present paper aims to investigate the atmospheric processes that led to an extreme rainfall resulting in destructive flash flooding and loss of life and property in the northwestern region of India on 8–9 July 2023. Exceptionally heavy precipitation occurred in the Western Himalaya and adjoining regions, leading to widespread disruption of communication, electricity, and inundation of houses. Flash floods, combined with debris flows, caused massive devastation in northern India, with the overall death toll surging to 91. Records from automatic weather stations highlight the severity of the event, with several stations breaking twenty-year records. This study examines the synoptic drivers, dynamic and thermodynamical features associated with the rainfall episode. Prior to the extreme precipitation, the monsoon trough migrated northwards, and its interaction with an incoming western disturbance (WD) passing over northwest India led to deep tropospheric instability over the Western Himalaya. The dynamic coupling of the monsoon trough with an unusually strong and slow-moving WD, caused by an extratropical cutoff low, facilitated moisture exchange, which further intensified, resulting in a severe rainstorm. The high moisture convergence was driven by extremely high amounts of moisture flux directed from the Bay of Bengal and Arabian Sea towards the Western Himalaya by the combined monsoon trough-WD system. Alongside this, an intense jet streak associated with the WD led to additional ageostrophic forcing over the western Hindukush Himalayas from 7 July, creating conducive conditions for deep ascent. Vorticity and moisture budget analyses highlight the main dynamical drivers (horizontal vorticity advection and vertical moisture transport) of the event.

Nepal is highly vulnerable to severe soil erosion driven by monsoonal rainfall and rugged terrains. Limitations in ground observation networks have hindered comprehensive, high-resolution national assessment of precipitation and rainfall-runoff erosivity (R-factor) across Nepal. This study systematically evaluated eight global gridded precipitation datasets (GPDs) against data from 152 weather stations, identifying the optimal precipitation dataset (TPHiPr) representing Nepal’s complex topography. Based on this high-quality dataset, we provided the first independent, long-term (1979–2020), high-resolution national-scale assessment of precipitation and the R-factor for Nepal. Our analysis reveals that 1996 marked a turning point in nationwide precipitation trends: annual precipitation shifted from a decreasing to an increasing one in the humid eastern and central regions, while the drier western region transitioned from an increasing to a decreasing trend, particularly during the dry season. A clear spatial divergence was observed between total precipitation and the R-factor, highlighting the dominant role of precipitation frequency and intensity. Extreme precipitation events intensified significantly (e.g., days with ≥25 mm rainfall increased by 0.2 days yr−1, and the 95th percentile precipitation threshold increased by 0.4 mm yr−1, p < 0.01), driving a nationwide increase in the R-factor (6.3 MJ mm ha−1 h−1 yr−2, p < 0.01), with high-altitude areas experiencing the most pronounced effects. We conclude that soil erosion risk has intensified nationwide due to increasing precipitation extremes. Watershed management must develop elevation-specific adaptation strategies that integrate climate science with practical solutions to address the dual challenges of intensified monsoon-driven erosion and growing dry-season water scarcity.

Understanding extreme precipitation (EP) evolution is crucial for global climate adaptation and hazardous disasters prevention. However, spatial non-stationarity of urbanization relationships with EP variations has been rarely discussed in a complex topographic context. Taking the city Liuzhou in China as the example, this study separately quantified the evolution of EP intensity, magnitude, duration, and frequency on different temporal scales with Innovative Trend Analysis (ITA). Based on a finer spatial (5 km grid) scale and multiple temporal (daily, daytime, nighttime, and 14 h) scale analyses, it innovatively identified spatially varying urbanization effects on EP with more details in different elevations. Our results indicate that: (1) from 2009 to 2023, EP events became more intense, persistent, and frequent, particularly for higher-grade EPs and in the steeper north of Liuzhou; (2) despite the globally negative correlations, spatial correlations between comprehensive urbanization (CUB) and each EP index on individual temporal scales were still explicitly categorized into four types using LISA maps—high-high, high-low, low-low, and low-high; (3) Geographically Weighted Regression (GWR) was demonstrated to precisely explain the response of most EP characteristics to multiple manifestation of urbanization with respect to population (POP), economy (GDP), and urban area (URP) expansion (adjusted R2: 0.5–0.8). The predictive accuracy of GWR on urbanization and EPs was spatially non-stationary and variable with temporal scales. The local influential strength and direction varied significantly with elevations. The most significant and positive influences of three urbanization predictors on EPs occurred at different elevation grades, respectively. Compared with POP and GDP, urban area percent (URP) was indicated to positively relate to EP changes in more areas of Liuzhou. The spatial and quantitative relationships between urbanization and EPs can help to guide effective urban planning and location-specific management of flood risks.

Abstract Accurate kilometer-scale forecasting of extreme precipitation (EP), defined as exceeding 20 mm/3h within 24-hour lead-time, is crucial for early warning systems. Although deep generative models have demonstrated effectiveness in bias correction and spatial-resolution enhancement for numerical weather prediction forecasts, their applicability to EP requires further investigation. In this study, the conditional generative adversarial network (CGAN) is developed to predict EP with 1-km spatial resolution. The datasets used for training, validation and testing comprise outputs from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), consisting of 11 EP-related physical variables and gridded observational precipitation data from May to October in 2019–2022. This model introduces a differentiable critical success index (CSI) loss function optimized for EP thresholds, which helps improve forecast accuracy. The proposed CGAN deterministic forecast outperforms both ECMWF and a baseline CGAN without the differentiable CSI loss, achieving 30% and 2.0% higher CSI scores, respectively. Additionally, its forecasts surpass the operational post-processing forecasts that use frequency matching methods on bias, radially averaged power spectral density and fractional skill score, demonstrating superior performance in kilometer-scale spatial distribution prediction. Our CGAN-based method also captures EP location, intensity and spatial patterns, closely matching observations for typical EP cases across different regions of China. This study demonstrates that CGAN-based post-processing represents a powerful approach to enhance early weather warning and disaster management capabilities on kilometer scales. Although there are still challenges regarding probabilistic forecasting and operational interpretability, this study provides a foundation for future advancements.

Rising temperatures, extreme precipitation events such as excessive or insufficient rainfall, increasing levels of carbon dioxide, and associated climatic factors will persistently impact crop growth and agricultural production. The warming temperatures have reduced the agricultural crop yields. Rice (Oryza sativa L.) is the major food crop, which is particularly susceptible to the effects of climate change. It is very important to accurately evaluate the impacts of climate change on rice growth and rice yield. In this study, the rice growth during 1981–2018 (baseline period) and 2041–2100 (future period) were separately simulated and compared within the CERES-Rice model (v4.6) using high-quality weather data, soil, and field experimental data at six agro-meteorological stations in Hainan Province. For the climate data of the future period, the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were applied, with carbon dioxide (CO2) fertilization effects considered. The adaptation strategies such as adjusting planting dates and switching rice cultivars were also assessed. The simulation results indicated that the early rice yields in the 2050s, 2070s, and 2090s were projected to decrease by 6.2%, 11.8%, and 20.0% when the CO2 fertilization effect was not considered, compared with the results of the baseline period, respectively, while late rice yields would decline by 9.9%, 23.4%, and 36.3% correspondingly. When accounting for the CO2 fertilization effect, the yields of early rice and late rice in the 2090s increased 16.9% and 6.2%, respectively. Regarding adaptation measures, adjusting planting dates and switching rice cultivars could increase early rice yields by 22.7% and 43.3%, respectively, while increasing late rice yields by 20.2% and 34.2% correspondingly. This study holds substantial scientific importance for elucidating the mechanistic pathways through which climate change influences rice productivity in tropical agro-ecosystems, and provides a critical foundation for formulating evidence-based adaptation strategies to mitigate climate-related risks in a timely manner. Cultivar substitution and temporal shifts in planting dates constituted two adaptation strategies for attenuating the adverse impacts of anthropogenic climate change on rice.

ABSTRACT Global warming is intensifying hydrological cycles, causing significant spatiotemporal variations in extreme precipitation. Since the 1980s, Northwest China has shifted from a warm‐dry to a warm‐wet climate regime, raising widespread concern. Employing skill metrics, this study quantitatively evaluates 23 statistically downscaled CMIP6 models from the NASA NEX‐GDDP dataset in simulating historical (1961–2014) extreme precipitation over Northwest China and, based on their performance, projects future changes in the mid‐ (2031–2060) and late‐21st century (2071–2100) under two scenarios. Six extreme precipitation indices are analysed: total wet‐day precipitation (PRCPTOT), very wet‐day precipitation (R95pTOT), maximum 5‐day precipitation (Rx5day), heavy precipitation days (R10mm), consecutive dry days (CDD) and consecutive wet days (CWD). Results show that most models reasonably capture spatial patterns (pattern correlation: 0.4–0.9), yet exhibit systematic dry biases (positive biases for CDD; negative biases for other indices). Interannual variability is better simulated in western subregions than eastern, particularly for PRCPTOT, R95pTOT, Rx5day and R10mm. Four models (CESM2, CESM2‐WACCM, CMCC‐CM2‐SR5 and EC‐Earth3‐Veg‐LR) demonstrate superior skill in spatiotemporal simulations. Projections from these best‐performing models indicate a mitigation of aridity and an increase in the frequency of extreme precipitation under SSP2‐4.5 and SSP5‐8.5 scenarios.

ABSTRACT In the past decades, the Tibetan Plateau has experienced rapid warming, accompanied by marked differences in seasonal extreme precipitation change. However, research focusing on the impact of human activities on the seasonal extreme precipitation is still limited. In this study, we employ different observations and CMIP6 models to investigate the relevant issue based on an optimal fingerprinting method. We found that both station data and ERA5 reanalysis data show similar detection results. In one‐signal analyses, the anthropogenic signal can be detected in the increasing trends of maximum 1‐day precipitation amount (Rx1day) and maximum consecutive 5‐day precipitation amount (Rx5day) in spring and winter but not in other seasons, whereas the signal of natural forcing cannot be detected in Rx1day and Rx5day changes during these two seasons. In two‐signal analyses, human activities on extreme precipitation changes over the Tibetan Plateau are noticeable during both spring and winter, with the human influence being more pronounced in spring. This study thus provides important evidence for human influences on the seasonal change of extreme precipitation over the Tibetan Plateau.

ABSTRACT Compound events (CEs), commonly defined as the “combination of multiple drivers and/or hazards that contributes to societal or environmental risk”, often result in amplified impacts compared to individual hazards. In order to estimate the return period of bivariate CEs, a novel nonparametric approach employing bivariate Generalized Pareto distributions (bi‐GPD) is proposed and compared to a copula‐based approach. Special attention is given to account for temporal dependencies and nonconcurrent compound events. The latter are defined as excess of variables over a threshold at a relatively close time. The return period of such bivariate events is carefully defined and closed‐form expressions are obtained for both approaches. Simulations reveal the bi‐GPD approach is effective in case of positive asymptotic dependence and should be avoided in case of asymptotic independence. The novel approach is then applied to ERA5 reanalysis data to analyze two types of compound events: a spatial CE with simultaneous floods due to accumulated precipitation across two large watersheds in France and a preconditioned CE consisting of a devastating flood triggered by extreme precipitation over a saturated soil.

ABSTRACT Global climate change has increased the risk of rainstorms to architectural heritage, while traditional rainwater management systems (TRMS) are sometimes insufficient to cope with extreme precipitation. This study analyzes disaster vulnerability in Chinese skywell-style architectural heritage (SAH). By examining the traditional ecological knowledge (TEK) utilized in SAH for rainstorm management and the primary damage patterns, a vulnerability assessment system was developed that encompasses five dimensions: drainage, infiltration, water storage, protection, and structure. Subsequently, the Analytic Hierarchy Process and the Entropy Weight Method were employed to ascertain indicator weights for constructing a vulnerability assessment model. This model was tested with Li’s Grand Ancestral Hall, which experienced the “16th June” rainstorm, and an obstacle degree model was applied to identify key vulnerability-enhancing indicators. Results indicate that drainage effectiveness, particularly outlet accessibility and topographic elevation, is critical in TRMS. The model aligns with actual damage conditions with an accuracy of 85.19%. The water storage system emerges as a key vulnerability-enhancing subsystem in TRMS. Moreover, vulnerability assessments should extend beyond individual buildings to consider the spatial layout of the entire architectural ensemble. This study presents a framework for applying TEK to climate adaptation, thereby enhancing the resilience of architectural heritage to climate change.

This article examines how value systems and place-based characteristics shape the ways in which local communities define, represent and prioritise climate risks in urban and rural settings. Drawing on 10 climate impact-chain assessments—co-developed through participatory processes with stakeholders in demonstration regions across Europe within the EU project VALORADA—we explore four hazard domains: urban warming, heatwaves, droughts and extreme precipitation. Building on previous literature and based on our observations, we show that the identification, definition and prioritisation of climate risks extend beyond biophysical aspects or existential threats and are also influenced by locally salient values, including sustainability, security, safety, identity, human health, cooperation and trust. These values can, at times, come into tension—particularly where the management of scarce resources (e.g., water) is contested, or where policy goals such as environmental conservation and economic development intersect within the same decision arena. We conclude by suggesting that addressing the challenge of integrating value-based and place-specific characteristics into climate risk assessments may benefit from illustrating how climate hazards influence local value frameworks and shape meaningful societal participation.