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Abstract Extreme precipitation can lead to major flooding, impacting human health and safety. Thus, reliable projections of population and GDP exposure to future extreme precipitation are imperative. Here, we quantify future precipitation characteristics from robust emergent constraint relationships between historical and future monthly precipitation extremes (99th percentile) across 19 CMIP6 models (r2 > 0.7 in 74–84% of 0.5° global land grids), and narrow uncertainty by 37.0–39.5% (absolute reduction being 0.753–0.774 mm/day). The constrained grid‐averaged future 99th percentile extreme is 6.96 ± 0.0059, 7.03 ± 0.0061, 7.11 ± 0.0063, and 7.29 ± 0.0067 mm/day, under SSP126, SSP245, SSP370, and SSP585, respectively, which exceeds historical extremes substantially in terms of intensity (12.9–19.7%) and frequency (1.6–2.3 times more). Future population and GDP exposed to 99th percentile extreme precipitation grow quickly, and are projected to exceed 1 million people in 27–40 countries and 10 billion USD (2005 Purchasing‐Power Parity) in 48–77 countries. Growth of future population exposure is dominated by an increase in extreme precipitation frequency rather than a rise in population, especially in developed countries. GDP exposure is controlled by the coupled effects of rapid socio‐economic development and significant shifts in precipitation frequency. Using indices of socio‐economic vulnerability, government effectiveness and economic freedom, we identify the unequal situation that high‐risk countries with high exposure are commonly characterized by low GDP per capita and high sociopolitical instability.

Abstract The quasi‐global availability of satellite‐based precipitation products (SPPs) holds significant potential for improving hydrological modeling skill. However, limited knowledge exists concerning the impacts of different SPP error type on hydrological modeling skill and their sensitivity across different climate zones. In this study, forcing data sets from 10 SPPs were collected to drive hydrological models during the period 2001–2018 for 366 catchments across China. Here, we analyze the impact of the SPP errors associated with different precipitation intensities (light, moderate, and heavy) and different precipitation signatures (magnitude, variance, and occurrence) on the performance of hydrological simulations, and rank the sensitivities of SPPs errors for four major Köppen‐Geiger climate zones. The results show that heavy precipitation in SPPs is generally associated with higher errors than light and moderate precipitation when compared to gauge‐based precipitation observations, but hydrological model skill is more sensitive to errors from moderate precipitation than from heavy precipitation. The probability of moderate precipitation detection was identified as the most sensitive metric in determining hydrological model performance, with sensitivities of 0.58, 0.39, 0.59, and 0.47 in the temperate, boreal, arid, and highland climate zones, respectively. The variance error and magnitude error for heavy precipitation from SPPs were also identified as sensitive factors for hydrological modeling in the temperate and arid climate zones, respectively. These findings are crucial for enhancing the understanding of interactions between SPPs uncertainty and hydrological simulations, leading to improved data accuracy of precipitation forcing and the identification of appropriate SPPs for hydrological simulation in China.

Abstract Fire weather compounded by extremely hot and dry conditions often severely impacts society and ecosystems. To mitigate and better adapt to these compound fire weather (CFW) events, a better understanding of recent and future CFW trends is needed. Here we show that in the period 1981–2020, the global average frequency and intensity of CFW events increased by 0.6 days/yr and 0.4%/yr, respectively. Increases in temperature and decreases in relative humidity were responsible for significant trends in the frequency of CFW events in 81.7% and 58.6% of locations, respectively. The same trends contributed to significant increases in CFW intensity in 72.1% and 57.9% of locations. We further demonstrate that anthropogenic climate change (due primarily to greenhouse gas emissions) has aggravated the frequency and intensity of CFW events, particularly in the Amazon region, with over 2‐fold and 1.3‐fold increases, respectively. Future projections reveal that other (individual) fire weather events are likely to shift toward CFW events accompanied by hot‐dry conditions, along with an expected rise in CFW intensity. Furthermore, the increased occurrence of CFW events is likely to substantially augment future population exposure to CFW conditions. Under the SSP5‐8.5 scenario, climate change is estimated to contribute 62.6% of the projected increase in population exposure to CFW events by the end of this century. Our findings underscore the urgent need for strong climate action to reduce population exposure to the growing threat of future fire weather events compounded with hot and dry conditions.

Abstract Decomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures.

Abstract Streamflow sensitivity to climate change is an important indicator for evaluating the effects of climate change on terrestrial water. This study analyzed the spatial pattern and seasonality of streamflow sensitivity to climate change for 425 catchments across China. The results indicate that precipitation is consistently a more important contributor than temperature to streamflow variability (mean streamflow sensitivity to annual precipitation [εP,a] = 0.92; absolute mean sensitivity to annual temperature [εT,a] = 0.05). Meanwhile, the seasonal sensitivity evaluation found that streamflow response to precipitation change in warm seasons (mean εP,w = 0.90) is significantly greater than in cool seasons (mean εP,c = 0.46), but the sensitivity of streamflow to changes in temperature was stronger in cool seasons than in warm seasons. The magnitude of streamflow sensitivity to temperature in cool seasons is significantly affected by elevation (Pearson's r = 0.31, P

Abstract Documenting the spatiotemporal changes in vegetation cover and hydrological cycle of the Earth system and understanding how they interact are important especially under climate warming. In this research, we quantified the changes in vegetation and evapotranspiration (E) across China during 1982–2015 and then revealed the complex relationships in climate–vegetation–evapotranspiration system. Results show that the upward trend in vegetation leaf area index (LAI) during 2000–2015 (an increase of 0.95% per year) was almost 8 times the trend during 1982–1999. The zones of the Loess Plateau and the Three‐North Shelter Forest Program are the most notable areas for LAI increases between these two periods, with increases of 11.65% and 2.87%, respectively. Increased LAI, along with the warming climate, has accelerated E across China in the past several decades, and the annual increase in the E rate was 0.34% (1.34 mm year−1) during 1982–1999 and 0.40% (1.62 mm year−1) during 2000–2015. The zones of the Loess Plateau and the karst landform are the most notable areas for transpiration increases, with individual increases of 10% and 5%, respectively. In general, the dominant causes for evaporation changes across all of China are temperature and precipitation, while the main reasons for transpiration changes include temperature, LAI, and sunshine duration. This study improves our understanding of the relationships within the climate–vegetation–evapotranspiration system and provides important support for future ecological policies across China.

Abstract Identifying climate change hotspot regions is critical for planning effective mitigation and adaptation activities. We use standard Euclidean distance (SED) to calculate integrated changes in precipitation and temperature means, interannual variability, and extremes between different future warming levels and a baseline period (1995–2014) using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble. We find consistent hotspots in the Amazon, central and western Africa, Indonesia and the Tibetan Plateau at warming levels of 1.5°C, 2°C, and 3°C for all scenarios explored; the Arctic, Central America and southern Africa emerge as hotspots at 4°C warming and at the end of the 21st century under two Shared Socioeconomic Pathways scenarios, SSP3‐7.0 and SSP5‐8.5. CMIP6 models show higher SED values than CMIP5, suggesting stronger aggregated effects of climate change under the new scenarios. Hotspot time of emergence (TOE) is further investigated; TOE is defined as the year when the climate change signal first exceeds the noise of natural variability in 21st century projections. The results indicate that TOEs for warming would occur over all primary hotspots, with the earliest occurring in the Arctic and Indonesia. For precipitation, TOEs occur before 2100 in the Arctic, the Tibetan Plateau and Central America. Results using a geographical detector model show that patterns of SED are shaped by extreme hot and dry occurrences at low‐to‐medium warming, while precipitation and temperature means and extreme precipitation occurrences are the dominant influences under the high emission scenario and at high warming levels.