Bioclimatic variables are essential indicators used across disciplines including ecology, geography, environmental science, and regional planning. While global datasets such as WorldClim and CHELSA provide climate information, the absence of regionally tailored datasets limits the ability to capture fine-scale climatic variability, which is critical for regional analysis. The Translate project addresses this gap by providing high-resolution (~1 km) observed and projected climate data for Ireland, enabling the generation of region-specific bioclimatic variables. The observational dataset was derived from long-term climate records using quality-controlled interpolation of national station data. An ensemble-based approach using ~200 Regional Climate Model (RCM) simulations was developed to generate percentile-based projections (10th, 50th, and 90th percentiles) across Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) and multiple time periods. These percentiles represent a range of plausible climate futures and support uncertainty assessment. Three complementary approaches were applied: full-ensemble, period-specific, and scenario-specific outputs across RCPs and time periods. The observational and full-ensemble datasets each comprise 57 GeoTIFFs, with additional outputs provided for period- and scenario-specific analyses.

This study assesses future hydrological responses of the Chitral River Basin (CRB), a high-mountain, glacier-fed catchment in northern Pakistan, under climate change. The Soil and Water Assessment Tool (SWAT) was forced with bias-corrected outputs from three CORDEX regional climate models under Representative Concentration Pathway 4.5 (RCP4.5) and Representative Concentration Pathway 8.5 (RCP8.5) scenarios for the period 2010-2099. Projected temperature increases range from 2.34°C to 5.23°C by the late century, while precipitation changes vary between 2.42% and 6%. These changes induce a shift in seasonal streamflow, with peak discharge advancing to June-July. Simulated streamflow responses indicate that warming may alter seasonal runoff timing through enhanced snow and ice melt processes. However, because the adopted SWAT configuration assumes static glacier area, projected late-century reductions should be interpreted as climate-driven hydrological responses rather than direct simulations of progressive glacier depletion. The results highlight substantial uncertainty in future annual flow magnitude across climate models and bias-correction methods, while consistently indicating sensitivity of runoff timing to climatic warming. These findings underline the need for adaptive water-management strategies in the Chitral River Basin.

Malaria remains a major challenge globally and in Africa, where climate change is likely to increase its prevalence among communities with low adaptive capacity. The aim of this study was to determine the characteristics of malaria dependence on meteorological drivers, and project incidences and spread of this disease in five district municipalities in Limpopo province, South Africa. We used data from weekly epidemiological reports on hospital admissions in the five municipalities to derive associations with corresponding regional temperature, rainfall, and evapotranspiration. Wavelet transform spectral analysis was applied to identify time lags characteristic for malaria development. We presumed that all the wavelet power spectra (WTS) peaks that we found in our data are characteristic times connected to the periods of development, distribution, and survival of either mosquitoes, as disease vectors, or the pathogens they transmit, or are the periods needed for human incubation of the disease. In this way, we were able to propose a regression model for the number of admissions cases, and to provide critical values of temperature, rainfall, and evapotranspiration that initiate the spread of the disease. Disease projections for 2021-2050 and 2051-2080 were made using Representative Concentration Pathways (RCPs): RCP2.6 and RCP8.5.

Climate-driven deepwater deoxygenation is a growing global concern for lake ecosystems. We developed a simple 1-D deepwater oxygen profile model to understand the underlying physical mechanisms and to quantify the required climate-adaptive interventions. It was applied to Lake Erken, Sweden, using hydrodynamic forcing under three Representative Concentration Pathway (RCP) scenarios. From 2020 to 2099, the annual anoxic factor (the number of days when the anoxic sediment area equals the surface area) projections show non-significant trends under RCP2.6, while increasing by 0.4 and 0.6 days year -1 decade -1 under RCP6.0 and RCP8.5, respectively. This climate-driven future deoxygenation, consistent across multiple oxygen metrics, mainly stems from prolonged stratification. To mitigate climate impacts by 2100, oxygen depletion rates, as a proxy for eutrophication, would need to be reduced by approximately 9–13%, 20–24% and 26–35% under RCP2.6, RCP6.0 and RCP8.5, respectively. This data-efficient framework can be applied to physically-dominated, seasonally stratified lakes. • A simple mechanistic model was developed for simulating deepwater oxygen profiles • Oxygen dynamics were mechanistically studied under different climate scenarios • The estimated reduction in oxygen depletion rate, could guide long-term mitigation • Prolonged stratification plays a prominent role in future anoxia progression • 35% lower oxygen depletion rate may mitigate future deoxygenation in Lake Erken

Agriculture is highly sensitive to climate variability, and ongoing warming is expected to modify the thermal conditions controlling fruit tree phenology and production. This study analyzes the spatiotemporal variability of winter chilling, spring heat accumulation, and spring frost probability across fruit-growing areas in Aragón (northeast Spain) using a regional, high-resolution agroclimatic approach. Chill portions (CP), growing degree hours (GDH), and spring frost probability occurrence (SFPO) were computed from daily gridded maximum and minimum temperature data at 1km² spatial resolution. Agroclimatic indicators were derived using statistical, non-experimental methods based on established chilling and forcing models and empirical temperature thresholds. Recent historical variability was characterized using overlapping 30-year historical climate periods while future climate conditions were assessed using daily temperature projections from an ensemble of 18 regional climate models (EURO-CORDEX), dynamically downscaled from global climate models and bias-corrected. Projections were analyzed for a historical reference period (1971-2000) and under the intermediate and high emissions scenario RCP4.5 and RCP8.5 for mid-century (2041-2070) and late-century (2071-2100) periods. Climate projections at ~ 5km spatial resolution were interpolated to 1km and bias-corrected using Empirical Quantile Mapping with high-resolution observational data as reference. Changes in CP and GDH distributions were quantified using spatial differences and standardized anomalies relative to historical conditions, and indicators were extracted at the location of existing fruit orchards. Results indicate that effective winter chilling remains within broad ranges compatible with fruit production across all scenarios, with persistent spatial contrasts between western and eastern sectors. In contrast, spring heat accumulation shows a strong and spatially coherent increase, particularly in low-elevation and eastern areas, indicating an increasing influence of spring temperatures on phenological dynamics. Although future scenarios project a substantial reduction in the probability of frost occurrence after early March, increasing heat accumulation may advance phenological development, potentially shifting frost exposure to earlier periods in late winter. Overall, the results indicate that agroclimatic conditions in Aragón are strongly structured by regional climatic gradients, and that climate change is likely to intensify spatial contrasts between colder and warmer production areas rather than producing uniform changes across the region. These results provide a regional agroclimatic framework that can support adaptation planning and the long-term management of fruit production under climate change.

ABSTRACT Sustainable environmental planning in spatially heterogeneous mountainous regions requires a deep understanding of how ecosystem service values (ESV) respond to future climate and socioeconomic uncertainties. This study introduces a scenario-driven framework that couples land use simulation with interpretable machine learning to capture spatiotemporal ESV dynamics. It is applied to Sichuan Province, China, a region with complex topography and diverse climate conditions. Land use changes from 2030 to 2050 are simulated under Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP) scenarios. Subsequently, the kernel Normalized Difference Vegetation Index (kNDVI) is used to improve ESV assessment accuracy. Furthermore, the eXtreme Gradient Boosting (XGBoost) model is applied alongside SHapley Additive exPlanations (SHAP) to analyze the influence and interactions of natural and socio-economic drivers. Results show a clear west-to-east gradient in ESVs. Higher values are found in high-altitude regions, while lower values appear in densely populated lowlands. ESVs increase under SSP1–2.6 and SSP2–4.5, but decline significantly under SSP5–8.5. This decline is most notable in low-elevation urban areas. Natural variables such as elevation, temperature, and vegetation dominate ESV patterns in uplands. In contrast, socioeconomic factors like GDP and population density are more influential in lowlands. Nonlinear and threshold responses are observed. High-emission scenarios pose greater ecological risks.

Due to changes in temperature and precipitation patterns, aquatic and semi-aquatic plant species are seriously threatened by climate change. This study evaluated how Marsilea minuta L., a small aquatic fern found in tropical and subtropical wetlands, would be affected by climate change across geographic regions. Maximum Entropy (MaxEnt) was used to simulate species distributions using 963 spatially filtered occurrence records and five bioclimatic variables (BIO1, BIO2, BIO6, BIO12, and BIO13), selected after a thorough multicollinearity analysis. The BCC-CSM1.1 general circulation model was used to anticipate future climate scenarios for 2050 and 2070 under Representative Concentration Pathways (RCP) 2.6 and 8.5. The model showed outstanding prediction ability (AUC = 0.91, TSS = 0.71). According to current distribution modeling, M. minuta has a limited climatic niche that is focused between 30°N and 30°S, with South Asia, Southeast Asia, and equatorial Africa providing the best habitat. The most significant predictor was found to be the annual mean temperature, which was followed by precipitation variables and the lowest temperature of the coldest month. With net habitat losses ranging from 7.3% under RCP 2.6 (2050) to 17.2% under RCP 8.5 (2070), future predictions showed progressive range contractions across all scenarios. The gains were limited to isolated areas at higher latitudes, whereas habitat losses were concentrated at range edges. According to limiting factor analysis, the minimum temperature of the coldest month limited 28.3% of areas, mostly at higher latitudes, whereas annual precipitation limited dispersion throughout 34.7% of the investigated areas. The Congo Basin and South Asia were found to be possible climate refugia that might sustain stable, favorable conditions in a variety of scenarios. According to response curve analysis, ideal conditions include low diurnal temperature ranges, frost-free winters, high wet-season precipitation surpassing 1200mm, and an annual mean temperature of 20-25°C. These findings emphasize M. minuta susceptibility to climate change and the necessity of proactive conservation measures, such as safeguarding recognized refugia. Improvement of wetland connectivity and incorporation of climate factors into more comprehensive wetland management initiatives. Because losses under high-emission scenarios significantly outweighed those under strict mitigation paths, the projected range reductions highlight the crucial relevance of greenhouse gas mitigation in limiting biodiversity consequences.

This study assesses the impacts of climate change (CC) on maize production in Bosnia and Herzegovina, comparing ten maize-producing municipalities and using Gradiška as a case study. Agroclimatic indicators and ISAREG-based soil water balance simulations were used to evaluate regional suitability for future maize production. Projections indicate substantial increases in average temperatures of 2 to 6 Celsius by the end of the century, depending on the RCP scenario, together with important reductions in accumulated mean precipitation, particularly during summer. Rising temperatures accelerate maize phenology, shortening growth cycles and enabling double-cropping opportunities for short-season cycles. Medium-season cycles may become feasible in most regions, while long-season cycles remain constrained in high-altitude areas due to thermal requirements. Rainfed maize in Gradiška is expected to face increased relative evapotranspiration deficits under future ‘hot & dry’ conditions, with potential relative yield losses due to water deficit of up to 12%. Irrigated maize shows a variation in irrigation requirements from −26% to +8% relative to the baseline, which reflects the combined effect of a shortened crop growth cycle under higher temperatures and increased evapotranspiration demand under drier conditions. Regions with high soil water-holding capacity are the most resilient, while areas with shallow soils or Mediterranean climates are more vulnerable under future conditions. The findings underscore the need for agronomic adaptation measures to the projected CC impacts, including supplemental irrigation, drought-tolerant cultivars, and potential adjustment of sowing.

Abstract Decadal variability of the Atlantic Meridional Overturning Circulation (AMOC-DV) serves as a key driver of low-frequency variability in meridional ocean heat transport, generating a characteristic dipole pattern in upper-ocean heat content (UOHC) between the subpolar gyre and Gulf Stream regions. This dynamical linkage underpins the predictability of the Atlantic Multidecadal Variability and its global climate impacts. However, by contrasting the historical (1920–2005) and Representative Concentration Pathway 8.5 (RCP8.5; 2006–2100) experiments in the Community Earth System Model version 1 Large Ensemble (CESM1-LE), we reveal a significant weakening of the correlation between AMOC-DV and North Atlantic UOHC under global warming, particularly in the subpolar gyre and Gulf Stream regions. The weaker correlation in a warmer future is primarily driven by a weakened AMOC-DV intensity and its related circulation anomalies, along with some climatological changes. For the subpolar gyre region, relative to the historical period, a positive AMOC-DV anomaly during the RCP8.5 period leads to less convergence of upper-ocean warm water, and a weaker mean AMOC further diminishes anomalous warm water transport into this region, both of which suppress AMOC-DV-associated warming. For the Gulf Stream region, a positive AMOC-DV anomaly during the RCP8.5 period, compared with the historical period, produces weaker deep western boundary current anomalies, a smaller southward shift of the Gulf Stream path, and consequently nearly no upper-ocean cooling. Our findings suggest a progressive disconnection between AMOC-DV and North Atlantic UOHC under global warming, challenging current frameworks that rely on AMOC initialization for North Atlantic decadal climate prediction.

Climate change will impact the distribution of daily deaths in Austria until the end of the century. This study examines the net effects of fewer cold and more-frequent hot days on daily mortality under different climate and demographic scenarios. Projected district-level mortality data and daily temperatures based on Representative Concentration Pathways (RCP4.5 and RCP8.5) are analyzed to estimate the number of attributable deaths for every fifth year due to heat and cold using district-wise temperature–effect estimates from a previous analysis. While the overall shape of the time course of temperature-attributable deaths depends mostly on the demographic developments (with the highest numbers of daily mortality mid-century), under all climate scenarios investigated, the increase in heat-attributable deaths will be more pronounced than the decrease in cold-attributable deaths. Contrary to common claims, shift in temperatures due to climate change already has a net negative effect on population health in Austria now.

Anthropogenic influences are reshaping ocean conditions, with rising sea surface temperatures, elevated CO2 concentrations, and shifting nutrient dynamics occurring alongside more frequent extreme weather events. While previous studies indicated that various planktonic taxa may be impacted by long-term environmental change, the response of the microbial community remains less explored, and we know particularly little about how short-term events such as marine heatwaves interact with long-term environmental changes. Here, we investigated the impact of a heatwave (+2°C for 5 days) on the microbial community in mesocosms mimicking ambient and future coastal conditions (1000 ppm CO2, +3°C, Nitrogen: Phosphate ratio of 25), according to IPCC predictions (high-emission scenario RCP 8.5) for 2100. We used 16S rRNA gene sequencing, fluorescence microscopy, and experimental approaches, to study the microbial community taxonomic composition, cell abundances, virus-like particle abundances, as well as cell division and mortality rates. Our results indicate that future coastal conditions may impact the microbial community composition, biodiversity, as well as the abundance of total bacterioplankton. In contrast, the marine heatwave we simulated had a smaller impact on the microbial community composition, and we did not observe any effect of the heatwave on virus-like particle counts and cell division rates. In conclusion, our findings suggest a certain resilience of the microbial community to short-term thermal disturbances.

Spatiotemporal evolution and ecological risk assessment of heavy metals in agricultural black soils of Northeast China.

The FAO CROPWAT 8.0 model, developed by the Food and Agriculture Organization (FAO), is widely used for estimating reference evapotranspiration (ETo), effective rainfall and stage‑wise crop water requirements based on climatic, soil and crop data. Anantapuramu District, located in the semi-arid Rayalaseema region of Andhra Pradesh, India, faces severe water scarcity due to high temperatures, low and erratic rainfall and frequent droughts. Agriculture, being the primary livelihood, is heavily dependent on irrigation, making precise estimation of crop water requirements (CWR) essential for sustainable water management. This study utilized the FAO CROPWAT 8.0 model to estimate historical and future crop water requirements for major crops including cotton, maize, groundnut (rabi and kharif) and vegetables. Historical climatic data (2014–2023) and projected weather datasets generated using MarkSim DSSAT with HADGEM2-ES and MIROC-ESM-CHEM models under RCP scenarios 2.6, 4.5, 6.0 and 8.5 for 2035, 2045 and 2055 were analyzed. Results indicated that cotton exhibits the highest water demand, followed by maize, groundnut rabi, vegetable crops and groundnut kharif. Temperature, relative humidity and wind speed significantly influenced CWR, with rising temperatures and reduced humidity increasing water demand. Under future climate scenarios, CWR is projected to increase gradually across all crops, with higher emissions scenarios (RCP 8.5) showing the largest increases. These findings highlight the need for adaptive irrigation strategies, efficient water management and the adoption of climate-resilient agricultural practices to sustain crop productivity in Anantapuramu under changing climatic conditions.

ABSTRACT Wheat yield and water demand are affected by the ongoing disturbance of climatic factors and greenhouse gases (GHG). As a result, the AquaCrop model's ability to anticipate climate change impacts on wheat harvests under full and deficit irrigation regimes in Sindh, Pakistan, was evaluated using wheat trials conducted between 2018 and 2020. However, for its validation, the results of the deficit irrigation treatment ITS 50 (50% controlled irrigation at the tillering stage) for both seasons were employed. The model efficiently estimated yield with a normalised root‐mean‐square error (NRMSE) of 13% and 17%, a Willmott's d ‐index of 98% and a Nash–Sutcliffe efficiency (NME) of 95% under full and deficit irrigation treatments, respectively. The simulation results revealed an adverse effect of climate change on the yield and water productivity of wheat. Over the century, the model predicted an increase of 6% to 7% in wheat yield under full (well‐watered) irrigation and a decrease of 10 to 12% in 25% reduced water applied depth (RAD) and 21% to 24% under 50% RAD scenarios for both representative concentration pathway RCPs (4.5 and 8.5). The overall wheat water productivity increased under a well‐watered irrigation regime by 16% in RCP8.5 compared with RCP4.5.

Harmful cyanobacterial blooms dominated by Microcystis aeruginosa represent an escalating threat to freshwater ecosystems and public health worldwide, driven by climate change and eutrophication. This study employs maximum entropy (MaxEnt) modeling to project the global distribution of suitable habitat for M. aeruginosa under current and future climatic conditions. We compiled 395 occurrence records from the Global Biodiversity Information Facility and field surveys, integrating them with six bioclimatic variables selected through rigorous multicollinearity filtering: Annual Mean Temperature (bio_1), Temperature Seasonality (bio_4), Maximum Temperature of Warmest Month (bio_5), Mean Temperature of Warmest Quarter (bio_10), Precipitation of Driest Month (bio_14), and Precipitation of Driest Quarter (bio_17). The MaxEnt model demonstrated excellent predictive performance (AUC = 0.91, TSS = 0.82), identifying temperate and subtropical regions across central Europe, eastern North America, East Asia, and southern Australia as current high-suitability zones. Limiting factor analysis revealed Temperature Seasonality as the dominant constraint on global distribution, emphasizing the critical role of inter-annual climatic variability in governing bloom dynamics. Projections under Representative Concentration Pathways RCP 2.6 and RCP 8.5 for 2050 and 2070 indicate substantial northward expansion of suitable habitat, particularly under high-emission scenarios. By 2070 under RCP 8.5, suitable habitat is projected to increase by 8.4% globally, with pronounced gains in high-latitude regions of Canada, northern Europe, and Russia, while some currently suitable subtropical areas experience habitat contraction due to intensified drought stress. These findings identify regions where future climatic conditions may become increasingly suitable for M. aeruginosa occurrence, providing a basis for prioritizing monitoring and precautionary water resource management strategies.

Pakistan ranks among the top ten nations globally that are likely to experience severe impacts from climate change and are facing critical food security challenges. Variability in temperature and rainfall alters the geographical suitability of land for crop cultivation. This study employed the Maximum Entropy (MaxEnt) model to forecast the effects of climate change and evaluate land suitability for Triticum aestivum L. (common wheat) production in Pakistan, where it is a major staple crop. Bioclimatic variables and occurrence data for two climatic scenarios, Representative Concentration Pathways (RCP) 4.5 and 8.5, from five General Circulation Models (GCMs) were used for the year 2070. The prime factors affecting the Triticum aestivum L. distribution are temperature seasonality, annual precipitation, and mean temperature of the warmest quarter. The findings indicate an average decline in highly suitable and moderately suitable areas, whereas an increase is observed in the least suitable areas in future scenarios. The highly suitable area for future distribution accounts for 26.78% and 19.67% under RCP 4.5 and RCP 8.5, respectively, highlighting a negative effect on prospective wheat production in Pakistan. The outcome of this research is of utmost significance for decision-makers to develop suitable adaptation and mitigation protocols to sustain wheat productivity under a changing climate.

This study focuses on the Maritime Continent within the Coordinated Regional Climate Downscaling Experiment–Southeast Asia (CORDEX–SEA) domain. This study provides the first systematic evaluation of present-day diurnal rainfall simulations over the CORDEX–SEA domain, and projects future changes under the Representative Concentration Pathway 8.5 scenario, emphasizing the Maritime Continent islands and adjacent coastal–offshore environments. The analysis covers both boreal summer and winter seasons and includes a diagnosis of the underlying dynamic and thermodynamic mechanisms driving the projected rainfall changes. High-resolution regional climate models better capture present-day diurnal rainfall amplitude, peak timing, and coastal-to-offshore propagation than their driving global climate models, with particularly marked improvements in propagation over the Maritime Continent. Future projections in the late 21st century indicate minimal change in diurnal timing over land but a widespread weakening of amplitude, especially in summer, with reduced coastal propagation. Over oceans, changes display a latitudinal pattern, with amplitude increases north of 10°N and decreases to the south. Physical mechanism analysis further reveals that reduced surface specific humidity and weakened surface wind convergence jointly suppress the diurnal rainfall peak, with thermodynamic effects dominating over Sumatra and dynamic effects over Borneo. These results provide new physical insights into the coupled roles of circulation and moisture in shaping the spatial heterogeneity of future diurnal rainfall changes over the Maritime Continent. • CORDEX improves simulation of diurnal rainfall propagation in the Maritime Continent. • Warming climate suppresses land diurnal rainfall, weakening future land–sea contrast. • Dynamic and thermodynamic shifts drive regionally varying diurnal rainfall weakening.

Rooftop photovoltaics are widely recognized for the carbon mitigation benefits, yet uncertainties persist regarding their future dynamics and broader impacts on water and land. Here we develop a city-level integrated framework to quantify rooftop photovoltaics potential across 349 Chinese cities, and evaluate evolving carbon-water-land tradeoffs under shared socioeconomic and representative concentration pathways. By 2050, projections across 15 combined scenarios indicate China's rooftop photovoltaic areas will increase by 9.2 to 34.8 percent relative to 2020 levels. Installed capacity ranges from 7.19 to 9.05 terawatts in 2050, expanding rapidly in eastern coastal cities. Under a moderate socioeconomic and climate scenario, the national carbon mitigation benefits peak during 2035 to 2040, accumulating 2.04 to 2.18 gigatons of carbon dioxide equivalent by 2050. In contrast, the water and land saving benefits continue to rise, reaching 16.5 to 17.4 cubic kilometers and 281 to 297 thousand square kilometers by 2050. These findings underscore the critical need for multidimensional planning to optimize future sustainable photovoltaics deployment.

Freshwater systems are among the most endangered ecosystems, with anthropogenic climate change causing detrimental ecological and economic impacts. Due to climate change, increased air temperatures will translate into the warming of rivers, and at the same time will alter flow regimes and increase evaporation and stochastic events. In this study, we used validated statistical water temperature models that predict average water temperatures (WTavg) from air temperature to project monthly and daily WTavg from 2025 to 2100 CE in the heavily polluted and over-abstracted Olifants River, Kruger National Park, Limpopo Province, South Africa, under the ‘business as usual’ Representative Concentration Pathway 8.5 scenario. The results from 16 General Circulation Models showed that monthly WTavg is likely to increase by 3.6 °C and showed summer months reaching up to 34–35 °C by 2100 CE. The daily results showed a similar increase of 3.7 °C by 2100 CE, with some extreme days reaching 42–44 °C. These results support similar research conducted within the Olifants catchment of the Limpopo Basin and add to the limited knowledge of freshwater climate change, especially in Africa. Climate change will ultimately alter the thermal and physical landscape of the Olifants River and this forecast highlights the need for further research on the potential detrimental consequences on freshwater biota, including possible local extinctions.

Extreme precipitation events are projected to change under climate change in terms of frequency, intensity and duration, which would cause serious impacts on water resources, agriculture, urban systems and socioeconomic conditions in the future. Based on 10 CMIP5 simulations statistically downscaled to 0.25° resolution through the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) initiative, seven precipitation climate extreme indices, as well as the probability ratio (PR) calculated by the Generalized Extreme Value (GEV) model for daily precipitation, were analyzed under scenarios RCP4.5 and RCP8.5. The results show that: (1) Annual precipitation is projected to increase significantly across China during the 21st century. The increasing rates are 1.4%/decade under RCP4.5 and 2.9%/decade under RCP8.5, respectively. The Tibetan Plateau exhibits the largest increase, particularly over the Karakoram Mountain area. Precipitation will also significantly increase in winter (13.59%/decade and 16.40%/decade) and spring (4.30%/decade and 6.33%/decade). (2) Precipitation extremes are projected to intensify markedly across China, with pronounced intensification in Southwest China and the Tibetan Plateau. (3) The more extreme the precipitation events, the greater the projected increase in the probability ratio (PR). It should be noted that the magnitude of the PR increase under RCP4.5 is significantly larger with respect to RCP8.5. These findings enhance the understanding of climate change and provide detailed regional-scale information to support adaptive policy-making.