In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses a machine learning approach to integrate and fit multiple models. The results show that the use of several evaluation metrics provides better results than the traditional ensemble median method. The correlation coefficients with the actual observations were found to improve from about 0.8 to 0.9, while the correlation coefficients of the precipitation amount (PRCPTOT), very heavy precipitation days (R20mm), and extreme precipitation intensity (SDII95) reached 0.95. Based on this, the precipitation simulations of moderate forced scenario for sharing socio-economic path (SSP2-4.5) from 27 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore potential changes in future extreme precipitation events in China and to calculate the distribution and trends of the PRCPTOT, extreme precipitation amount (R95pTOT), maximum consecutive 5-day precipitation (Rx5day), precipitation intensity (SDII), SDII95, and R20mm for the early 21st century (2023–2050), mid-21st century (2051–2075), and late 21st century (2076–2100), respectively. The results showed that the most significant increase in extreme precipitation indices is expected to occur by the end of the century, with the R95pTOT, Rx5day, and SDII95 increasing by 13.73%, 9.43%, and 9.34%, respectively, from the base period. The remaining three precipitation indexes, the PRCPTOT, SDII, and R20mm, also showed increases of 8.77%, 6.84%, and 4.02%, respectively. Additionally, there were apparent differences in the spatial variation of extreme precipitation. There were significant increasing trends of extreme precipitation indexes in central China and northeast China in the three periods, among which the total annual precipitation showed an increasing trend in central and northern China and a decreasing trend in western and south China. An increasing trend of annual precipitation intensity was found to be mainly concentrated in central China and south China, and the annual precipitation frequency showed a larger increasing trend at the beginning of this century. The annual precipitation frequency showed an increasing trend in the early part of this century. In general, all the indices showed an overall increasing trend in the future period, with the PRCPTOT, Rx5day, and SDII95 showing the most significant overall increasing trends.

Southeast Asia (SEA) is experiencing rapid warming, leading to more extreme heatwaves. Sustained compound heatwaves, with high temperatures during day and at night, pose profound threats in highly vulnerable regions, resulting in great stress on society. We estimated the changes in the compound heatwave characteristics and population exposure over SEA at the end of the 21st century based on the model outputs from the Coupled Model Intercomparison Project Phase 6. Results show that the projected compound heatwaves have significant intensified over SEA linked to increasing greenhouse gas emissions. Contemporary younger generations will face more potential risks than their parents' generation. A child born in the 2010s will experience 1,000 discrete heatwaves in their lifetime, a threefold increase compared with the 1980s. Climate change and population growth combine to drive increased population exposure. The climate effect accounts for 125% in the 0–24‐year‐old cohort, whereas the interaction effect accounts for 85% in the 75+ age group. The relative importance of effects evolves dynamically across age groups, gradually shifting from a predominance of climate effects to a synergy of the climate and population effects. Significant regional inequalities exist in the increased population exposure over SEA. The largest increase occurs in Indonesia, where the aggregate exposure ranges from 45 billion person‐days under the SSP1‐2.6 scenario to 81 and 108 billion person‐days, respectively, in the higher SSP2‐4.5 and SSP5‐8.5 scenarios. The study emphasizes the need for the SEA countries to focus on heat‐stress adaptation strategies, while also working toward fulfilling emission reduction commitments.

Surface air temperatures (SATs) derived from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim and CERA‐20C reanalysis data sets are compared with data from 43 observation stations in Sichuan for 1979–2010. The results show (a) the temperatures from the ERA‐Interim and CERA‐20C data sets are strongly correlated with those from the observation stations, although significant cold biases are seen on both annual and seasonal timescales. (b) The biases in SATs are predominately influenced by the differences between the actual topography and the topography used in the reanalysis models. Larger differences in temperature are observed in the plateau and mountainous regions of Sichuan. We confirmed larger SAT biases at high altitudes by categorizing the elevation into four bands, each with a spacing of 1,000 m. (c) We reduced the biases resulting from elevation by using an elevation correction method with internal lapse rates derived from different reanalysis pressure levels. The annual mean bias was reduced from −2.86 to −0.75°C for the ERA‐Interim data set and from −5.27 to −2.21°C for the CERA‐20C data set. After calibration, the correlation coefficients between the difference in SAT (observed minus reanalysis data) and the difference in elevation (station elevation minus model elevation) decreased from −0.97 and −0.91 to −0.29 and −0.30 for the ERA‐Interim and CERA‐20C data sets, respectively. These significant differences should not be ignored in the application of reanalysis data sets to climate research. The evaluation and calibration of reanalysis data sets are essential before making assessments of regional climate change, especially over regions with complex topography.