Urban expansion and precipitation variability synergistically intensify runoff generation and shorten the time of concentration. This study quantitatively assessed the impact of surface runoff and runoff coefficients in Gdynia, and particularly in the Kacza River basin. A synthetic precipitation event with a 1% probability of occurrence (100-year return period) for durations of 15 min, 60 min , and 24 h is analyzed together with daily precipitation projections under RCP4.5 and RCP8.5 scenarios. Results indicate a decline in agricultural land, green areas, and sparsely vegetated (bare) surfaces by 7.39%, 6.54%, and 2.16%, respectively, between 1985 and 2024. Over the same period, forest cover and built-up areas increased by 5.87% and 10.21%. Notably, 5.37% of the expansion in built-up area occurred within districts located in the Kacza River basin. Correspondingly, surface runoff increased from 0.69 to 1.63 mm (15-min event), 3.27 to 5.27 mm (60-min event), and 25.88 to 31.69 mm (24-h event), reflecting reduced infiltration capacity and a greater share of precipitation converting directly into runoff as urbanization progressed. Under future precipitation projections, surface runoff is expected to rise by 1.09% (2050) and 2.37% (2100) under RCP4.5, and by 2.70% (2050) and 7.33% (2100) under RCP8.5 relative to the baseline. Runoff coefficients will exhibit a comparable pattern, increasing by 0.77% and 1.55% under RCP4.5 and by 1.48% and 2.96% under RCP8.5 for 2050 and 2100 respectively. Despite uncertainties in event representativeness and spatial detail linked to synthetic precipitation event assumptions and historical land-use re-mapping, the methods and findings are expected to add insights into scientific knowledge as well as contributes to water conservation and adaptive urban planning. Currently, changes in runoff patterns are estimated under LULC and projected precipitation changes using daily cumulative synthetic precipitation event as a baseline; future research will extend this framework by simulating outflow dynamics and evaluating the role of technical solutions through hydrological modeling.

Abstract Future precipitation projections rely heavily on climate models, underscoring the need to evaluate their ability to simulate historical precipitation changes. Using multiple atmospheric models and ensemble simulations, we estimate the forced signals driven by sea surface warming and the direct effects of greenhouse gases and aerosols, as well as the atmospheric internal variability in precipitation trends since 1980. We find that forced precipitation trends are generally consistent across models, while atmospheric internal variability significantly influences regional patterns. Additionally, a few model members can reasonably well reproduce the observed pattern of precipitation trends. We highlight some regional wetting and drying are likely driven by forcings rather than the atmospheric internal variability. Zonal‐mean trends over land reveal a “wet gets wetter, dry gets drier” paradigm in the Northern Hemisphere, while the Southern Hemisphere shows drying near 45°S associated with jet stream shifts. These results help improve our understanding of historical precipitation changes.

Since the 1990s, inter-tropical Africa (ITA) has experienced consecutive calamitous droughts during the boreal spring. Although the observed precipitation regime changes have been attributed to tropical Indian Ocean-western Pacific warming and/or tropical Pacific La Niña-like cooling, the model-projected past-to-future widespread wetting response to anthropogenic warming overshadows qualitative attributions of decadal shifts in historical precipitation regimes and the reliability of near-term projections. The causes of ITA precipitation regime shifts and the likelihood of their future continuation remain unclear. Here, we reveal that the observed monopolar precipitation changes in ITA are primarily driven by the tropical easterly jet (TEJ)-dominated pattern, with a secondary contribution from the intertropical convergence zone (ITCZ)-mediated pattern. The Indo-Pacific warming-induced TEJ strengthening favors a monopolar drying trend from 1950 to 2022, while the northward-shifted ITCZ drives a west drying-east wetting dipolar pattern. Considering an observational TEJ constraint, an accelerated TEJ with an amplitude of -2 standard deviations could cause an almost threefold increase in extreme drying trends in the near term (2026–2045). Instead, ITA could face a higher likelihood of extreme wetting tendency due to a near-term TEJ weakening. Our findings underscore the importance of realistic TEJ simulations in enhancing confidence in future precipitation projections across hydroclimate-vulnerable Africa.

As Indonesia's new capital (hereafter called Nusantara City or NC) aspires to become a leading sustainable city, it must be developed with resilience to climate change. This study investigates the future seasonal extreme precipitation conditions in NC using a set of climate projections from the CORDEX-SEA Regional Climate Models (RCMs). The analysis integrates four Global Climate Models (GCMs) downscaled using two RCMs: RegCM4 and RCA4. The model results were analyzed for the periods 1970–2005 (historical), 2011–2040 (early century), 2041–2070 (mid-century), and 2071–2099 (late century) under both the low-emission (RCP 2.6) and high-emission (RCP 8.5) scenarios. Model evaluation using Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 07B data shows that RegCM4-based downscaling more accurately captures mean rainfall, seasonal patterns, and extreme precipitation indices in NC than RCA4. The ensemble average of three RegCM4 runs (HadGEM2-ES, MPI-ESM-MR, and NorESM1) indicates significant increases in extreme precipitation indices under RCP 8.5 from the mid to late century. By the late century, indices such as Maximum 5-Day Precipitation (RX5days), Consecutive Dry Days (CDD), and Number of Very Heavy Precipitation Days (R50mm) are projected to increase by 5.88 %, 41.12 %, and 29.30 %, respectively, while Total Precipitation (PRPTOT), Consecutive Wet Days (CWD), and Number of Heavy Precipitation Days (R20mm) decrease by −15.69 %, −21.93 %, and − 13.78 %. Rainfall during the dry season (June–September) is projected to decline by approximately 17 % under RCP 2.6 and 25 % under RCP 8.5. Historically, El Niño has triggered droughts during the dry season in Kalimantan, and under future warming, severe droughts resembling those driven by El Niño may occur even in its absence. Furthermore, the divergent projections resulting from the various models employed here suggest the critical role of model selection in hydrological impact analysis, as different models can produce strongly divergent estimates of future extreme rainfall. • Future precipitation extremes and seasonal patterns at Indonesia's planned new capital city projected using CORDEX-SEA models. • Precipitation totals are projected to significantly decline under both RCP 2.6 and RCP 8.5 scenarios from mid-century (2041–2070) through 2100. • The decline of precipitation is especially pronounced during the dry season (June–September). • RegCM4 models better capture mean rainfall, extreme precipitation indices, and seasonal variability than RCA4.

Due to the uncertainty of climate change and ongoing urbanization, as seen in Nusantara (the new capital city of Indonesia), conducting flood risk assessments by integrating these factors is essential. This study evaluates both the historical and future projections of land cover and precipitation in the Sanggai Basin (site of Nusantara). Land cover in 2055 was projected using the CA-ANN (Cellular Automata – Artificial Neural Network) method integrated in MOLUSCE. The Quantile Delta Mapping (QDM) method was applied for bias correction of CHIRPS (Climate Hazards Group InfraRed Precipitation) and three global climate models (EC-Earth3, EC-Earth3-Veg, and MRI-ESM2-0) derived from CMIP6 (Coupled Model Intercomparison Project Phase Sixth). Five experiments based on Shared Socioeconomic Pathways (SSP) were analyzed for each model to assess the impact of future climate conditions on extreme rainfall: SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0, and SSP5-8.5. A semi-distributed rainfall-runoff model, coupled with a flood inundation simulation, was used to generate flood inundation maps. This study projected a substantial growth of built-up areas by 2055, resulting in increased floodplain extent, further intensified by climate change. The largest inundation is observed under the combination of SSP5-8.5 and projected land cover, with 1, 817.8 ha predicted to be flooded under a 200-year return period rainfall. These findings provide valuable insights into flood mitigation strategies, regional planning, and climate change adaptation policies.

Abstract The Antarctic Ice Sheet is a critical driver of global sea level rise, yet future projections of Antarctic precipitation remain highly uncertain, posing challenges to modeling ice sheet changes.This study examines uncertainties in Antarctic precipitation projections using simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and investigates their underlying sources. We find that uncertainties in precipitation projections are substantially larger than those for surface temperature. Integrated precipitation uncertainties are strongly linked to uncertainties in both global and Antarctic surface temperatures, while atmospheric circulation-particularly the Pacific South American (PSA) modes-plays a critical role in regional precipitation patterns of precipitation uncertainties. Further analysis reveals that tropical sea surface temperatures contribute to uncertainties in Antarctic precipitation patterns through atmospheric teleconnections.These findings highlight the need to improve polar processes in climate models to reduce uncertainties in Antarctic precipitation projections and better predict ice sheet contributions to sea level rise.

South Korea (SK), East Asia The increasing number and intensity of precipitation make South Korea highly vulnerable to climate change. Therefore, accurate predictions of future extreme events are essential for this region. In the present study, we demonstrate that the models have some significant biases in the projection of precipitation extremes and that the bias correction using quantile delta mapping resolves most of these issues. Additionally, the future projections of extreme precipitation are analyzed by using the generalized extreme value theory. The present study demonstrates that the values associated with specific return periods are projected to increase, signifying intensification of precipitation and the higher likelihood of the extreme events occurring in the future. Therefore, it is of utmost importance to consider using forecasted future design frequencies instead of ones based on the historical record when planning the flood defenses and agricultural development. • South Korea is highly vulnerable to extreme precipitation caused by climate change. • Regional Climate Models can simulate the extremes appropriately. • Extremes are likely to increase in future; larger increase in high emission scenario. • It is important to use projected values rather than historical ones.

Urban flooding poses an escalating threat to riverine cities in Southern Asia’s tropical regions, primarily driven by rapid urban expansion. This study develops future projections of Intensity–Duration–Frequency (IDF) curves for major urban centers in Punjab, Pakistan, utilizing downscaled satellite-derived precipitation data. Baseline IDF curves were established using historical rainfall records from multiple meteorological stations. Among eight General Circulation Models (GCMs) assessed, EC-Earth3-Veg-LR demonstrated the highest skill in capturing extreme rainfall patterns relevant to the region. Future precipitation projections from this model were downscaled using the Equidistant Quantile Matching (EQM) technique and applied to generate IDF curves under two CMIP6 scenarios: SSP2-4.5 and SSP5-8.5. The results reveal a substantial increase in extreme rainfall intensities, particularly under the SSP5-8.5 scenario, with projected 100-year return period rainfall intensities rising by approximately 30–55% across key cities. The downscaled projections reveal more pronounced variations than the raw GCM outputs, emphasizing the importance of high-resolution climate data for accurate regional hydrological risk evaluation and effective urban flood resilience planning.

Assessing and reducing uncertainties in future mean and extreme precipitation projections over China

Future Projection of Extreme Temperature, Precipitation and Runoff Indices in the Aga-Foua-Djilas Basin (Senegal) under Global Warming

Abstract A novel quintile multi-model ensemble (QMME) framework is introduced to improve future precipitation projections from MMEs. The QMME method divides daily precipitation into five quintiles, thereby capturing not only extreme rainfall but also the full spectrum of precipitation intensities. This approach surpasses conventional ensembles, such as the equal MME (EMME) and the weighted MME (WMME), in accurately reflecting the distinct characteristics of individual climate models. To develop the QMME, this study first applies empirical quantile mapping to correct biases in daily precipitation outputs from 14 coupled model intercomparison project 6 (CMIP6) general circulation models (GCMs). Historical observations (1980–2014) from 61-gauge stations in South Korea are then used to evaluate GCM performance within each quintile. The QMME framework integrates each GCM’s historical performance and future uncertainty into a quintile-specific weights and combines them with XGBoost to generate enhanced precipitation time series. Four shared socioeconomic pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are considered to assess a range of future scenarios. As a result, the QMME consistently outperforms both EMME and WMME in capturing both extreme precipitation events and moderate rainfall conditions. An evaluation using reliability ensemble averaging confirms that the QMME more effectively accounts for inter-model variability and reduces uncertainty, thus providing robust projections of future precipitation. This quintile-based methodology can be readily extended to other hydroclimatic variables and geographic regions, offering significant potential for improving climate impact assessments and guiding risk management in water resources planning.

Abstract This study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.

This study aimed to evaluate precipitation estimates over the Brazilian Legal Amazon (BLA) using high-resolution historical simulations from the MPI-ESM1-2-HR climate model, before and after regionalization with the RegCM4.7.1 model. Continuous 32-year simulations (1981-2012) were compared against observed precipitation data on a regular 0.5° × 0.5° grid over the BLA. Six experiments were conducted: (1) MPI, comparing raw MPI-ESM1-2-HR precipitation with observations; (2) REG, comparing regionalized MPI-ESM1-2-HR precipitation via RegCM4.7.1 with observations; and (3-6) four experiments applying two bias correction methods, canonical correlation analysis (CCA) and principal component regression (PCR), to the MPI and REG out-puts, resulting in MPI-CCA, MPI-PCR, REG-CCA, and REG-PCR experiments. Monthly evaluations revealed very low average correlations (r) between the uncorrected simulations and observations: 0.008 for MPI and 0.013 for REG, with mean ab-solute errors (MAE) of 80 mm and 120 mm, and root mean square errors (RMSE) of 97 mm and 143 mm, respectively, indicating poor representation of observed climatology. However, the application of CCA and PCR substantially improved the simulations. MPI-CCA achieved r = 0.36, MAE = 43 mm, and RMSE = 54 mm, while REG-CCA reached r = 0.41, MAE = 42 mm, and RMSE = 53 mm. The best performance was observed with PCR: MPI-PCR showed r = 0.47, MAE = 40 mm, and RMSE = 51 mm, whereas REG-PCR obtained the highest accuracy with r = 0.52, MAE = 39 mm, and RMSE = 50 mm. These improvements were corroborated by Kling-Gupta Efficiency (KGE) analysis, reinforcing its value as a metric for precipitation simulation assessment. Among all months, REG-PCR achieved superior correlation and lower errors in 8 out of 12 months (February, March, April, July, September, October, November, and December). MPI-PCR performed better in January, June, and August, while REG-CCA stood out only in May. These findings underscore the importance of bias correction, particularly PCR, in reducing uncertainties in future precipitation projections for the BLA. The results highlight the potential for applying PCR to model outputs to improve projections of climate extremes, thereby supporting strategic planning across multiple sectors in this critical region.

The accelerated changes in climate resulting in more frequent and disruptive floods necessitate broader perspectives of levee vulnerability assessment. This paper aims to advance levee vulnerability curves using global sensitivity analysis to identify critical inputs for developing multi-variable fragilities. This analysis is efficient and effective for skewed data such as extreme precipitation. The study categorizes inputs into geometry, precipitation, and soil characteristics, generating 30,000 scenarios for analysis. A transient unsaturated seepage analysis is conducted to examine different failure modes such as piping, erosion, and overflow, as well as erosion initiation and enlargement time and locations for each scenario. Results show that, in addition to the initial upstream water level and precipitation characteristics, soil properties—such as gravel and clay content, along with water retention parameters—are crucial for developing fragility curves across different soil types. Additionally, comparing fragility curves for historical data and future precipitation projections highlights the importance of integrating these projections into levee risk analysis for the next 30 years. As a practical implication, these fragility curves are applied to calculate failure probabilities for a levee case study. This research would support the integration of levee vulnerability assessments with social factors and stakeholder perspectives which also increases the applicability of fragility functions in flood risk mitigation.

ABSTRACTPrevious studies have indicated a large model disagreement in the future projections of precipitation changes over Mediterranean climate (MedClim) regions worldwide. The majority of these highly populated regions have experienced major droughts in the recent decades, raising concerns about future precipitation changes and their impacts. Here, we examine precipitation projections in five MedClim regions from the CMIP6 ensemble, focusing on model consensus regarding the direction and magnitude of future precipitation changes. Our analysis spans the period 2050–2079 relative to 1970–1999, considering two climate change scenarios (SSP2‐4.5 and SSP5‐8.5) across the Mediterranean Basin (MED), California (CAL), the central coast of Chile (SAA), the Cape Province area of South Africa (SAF), and southwest Australia (AUS). The CMIP6 ensemble mean suggests that annual mean cumulative precipitation will decrease over all the regions except northern California, primarily due to a reduction in winter precipitation, and except over the Mediterranean Basin, where the most significant decrease occurs in autumn. Model agreement on the sign of future precipitation changes is high where the ensemble mean indicates a decrease, but lower where an increase or no changes are projected. Additionally, consecutive dry days (CDD) are expected to increase across all regions, whilst consecutive wet days (CWD) are expected to decrease. Maximum 1‐day precipitation is projected to increase uniformly across all regions. We conclude that despite substantial improvements to the new CMIP6 generation of models, the model spread in future precipitation projections in MedClim regions continues to be high. Impact studies need to account for these uncertainties and consider the whole intermodel range of projected precipitation changes.

Abstract Atmospheric rivers (ARs) are narrow regions of strong water vapor transport in the atmosphere that can cause beneficial and hazardous hydrological impacts. One of the challenges of evaluating future AR projections is in defining future Integrated Water Vapor Transport (IVT) thresholds to use to identify ARs. The two main methods are to use a percentile of IVT based on the historical climate (fixed) or a percentile based on the future climate (relative). Although most global studies of future ARs use a fixed method, the choice of thresholding method can lead to different future projections. This study assesses the sensitivity of future projections (2080–2100) of AR frequency and precipitation impacts over Australia to the IVT thresholding method using Coupled Model Intercomparison Project 6 data under a moderate (SSP245) and high (SSP585) emissions scenario. We found that both thresholding methods lead to an increase (or no change) in AR frequency but there is considerable variation in the magnitude of the projected increase in AR frequency. Both methods suggest that heavy AR‐associated precipitation will increase along with the occurrence of no rain during AR events over southeast Australia, whereas there may be a reduction in both light and heavy AR precipitation over southwest Australia. Using a fixed method leads to a drier projection for AR‐associated precipitation and identifies weaker ARs. As the driest inhabited continent, understanding future precipitation and how rain‐bearing weather systems, such as ARs, may change in a warmer climate is important for managing risk to Australia's future water availability.

ABSTRACT Climate change driven by global warming alters the fundamental characteristics of climate variables. Comprehending changes in extreme events and assessing their variation during the observed and projected periods is crucial for effective climate change adaptation and water resource management. In the Asia–Pacific region, due to the global oceanic and atmospheric patterns influencing precipitation variability, the study explores the extent to which teleconnection patterns impact precipitation extremes including consecutive dry days (CDD), days with rainfall exceeding 10 mm (R10mm), total precipitation (PRCPTOT), maximum single‐day and 5‐day precipitation (Rx1day and Rx5day), as well as the percentages of total precipitation from 90th, 95th and 99th percentiles (R90pTOT, R95pTOT and R99pTOT). To understand potential connections between extreme indices and global drivers, the observed changes show negative correlations with CDD during the monsoon season. The correlation of individual indices shows that all indices are highly influenced by El Niño–Southern Oscillation (ENSO) and Pacific North Index (PNA) with a correlation of 0.52–0.67 during the pre‐monsoon season, whereas Pacific Decadal Oscillation (PDO), Dipole Mode Index (DMI) and ENSO have positive correlation during the post‐monsoon season. The association of the teleconnection and the projected precipitation indices demonstrates a robust correlation with extreme indices during the monsoon season, particularly in association with North Atlantic Oscillation (NAO) and PDO. In contrast, ENSO shows no notable correlation during the pre‐monsoon season. Projections of future extreme precipitation are evaluated for the impact of variation in global teleconnections in contributing to add deeper understanding of mechanisms driving extreme precipitation events. This study serves as a valuable reference for researchers, providing a foundation for developing effective mitigation strategies that support sustainable development.

Daily precipitation time series exhibit intermittent periods of high variability separated by periods of no rain, posing challenges to correct projected precipitation. To improve projected changes in probabilities of flooding and drought, it is critically important to improve temporal correlations of the precipitation time series. Previous work introduced a Time Variability Correction (TVC) method, which quantified and corrected time variability errors at differing time scales. This study extends TVC to post-process daily precipitation projections from 28 CMIP6 models over Australia, introducing a new mean adjustment procedure to eliminate negative precipitation values while ensuring that both the mean and variability of the final series aligns with the observations in the historical training period. The new TVC mean-adjusted (TVC-ma) method preserves each model’s projected change in timescale covariances, and our analysis reveals interesting differences among CMIP6 projections of changes in time-scale-dependent variances. TVC-ma is evaluated using a leave-one-out model-as-truth setup. Results reveal that, in most cases, TVC-ma significantly improves the mean, variance, lag correlations, and projections of climate indices related to persistent, heavy, and low rainfall extremes compared to raw models. When applied to future precipitation projections for Australia, TVC-ma projects pronounced increases in prolonged dry periods and maximum 1-day and 5-day precipitation amounts under the high-emission scenario relative to the low-emission scenario. Compared to the historical period, corrected projections under the high-emission scenario show drier conditions in parts of Western Australia, greater variability, extended durations of consecutive dry days and increased multi-day precipitation extremes across most regions of the continent.

This study examines present and future projections of precipitation and evapotranspiration for South America, focusing on small regions with distinct environmental and climatic characteristics. The objective is to understand future water availability across the continent and assess the role of model resolution in shaping these projections (2015–2050). Five coupled Global Coupled Climate Models (GCMs) from CMIP6 (HighResMIP) with low (∼70 km) and high (∼25 km) horizontal resolutions were analyzed: HadGEM3, MPI, CMCC, EC-Earth3P, and HiRAM. For the present climate (1979–2014), statistical indices were applied to identify the primary effects of model resolution on the ability to capture regional climate characteristics by comparing simulations with GPCC and ERA5 reference dataset. The HighResMIP models demonstrated strong performance in simulating the precipitation climatology, with higher-resolution versions increasing the spatial pattern correlation (until 0.90) and reducing the RMSE (1.32 to 1.82 mm/day) and biases. These spatial correlations improved further (until 0.93) when only precipitation over continental areas is analyzed. At the regional scale, the precipitation annual cycles in high-resolution simulations is consistently improved over the northeast Brazil, La Plata basin and eastern Amazon basin, while in others regions the differences between high and low resolutions are smaller as well as occurs for evapotranspiration annual cycles except in eastern Amazon. In both resolutions, projections indicate a future intensification of the dry season, with a rainfall decrease of over 30% in central South America. For austral summer and autumn, a future increase of rainfall is projected for Pacific and Atlantic branches and southward of 25°S, including La Plata basin. The future changes in water resources present some differences associated with model resolution. The high-resolution projects water resources increased in an extensive strip from central Argentina to northeastern Brazil and decreased over the Amazon basin. For low-resolution projections this change pattern is not so evident due large divergence between members. The signal of precipitation and evapotranspiration trends controlling the water resources trends at a regional scale, previously found only in regional climate projections, is consistent with those in high-resolution HighResMIP simulations.

Future precipitation projections and model evaluation in the Hengduan Mountains based on CMIP6.