The consecutive heat wave and heavy rainfall (CHWHR) events, defined as the occurrence of heat waves followed by heavy rainfall, can cause more damages than individual extremes. Nevertheless, this type of compound event has not been diagnosed systematically. Here we examine the occurrence of CHWHR events and underlying characteristics. We find 22% of land areas experienced statistically significant CHWHR events within 7 days in China during 1981–2005, with an average 26% of heat waves being followed by heavy rainfall (vs. 10% expected by chance). More importantly, the shorter and hotter heat waves are more likely to be followed by heavy rainfall than other heat waves. This phenomenon is associated with atmospheric convection and moisture convergence. In addition, climate projection shows the CHWHR events will occur more frequently and abruptly in China throughout the 21st century, which contribute to the increased compound risk of back‐to‐back heat waves and flash floods.

As a critical air pollutant, PM2.5 is proved to be associated with numerous adverse health impacts and pose serious challenges to human life. This situation is especially important for China as the most populous and one of the heaviest PM2.5 polluted country in the world. However, health burden estimations reported for China in previous studies may be biased due to the usage of PM2.5 concentrations at a coarsely spatial resolution, as well as the ignorance of the spatial discrepancies of parameters (e.g. respiratory rate) employed in the exposure-response function. This study therefore utilized a hybrid remote sensing-geostatistical approach to refine PM2.5 concentrations at 1 km resolution across mainland China from 2013 to 2017. Meanwhile, nationwide exposure parameters were for the first time introduced to weight the integrated exposure response (IER) function to calculate and spatially reallocate the corresponding PM2.5-attributable premature deaths at 1 km resolution. Results showed that annually averaged PM2.5 concentrations in mainland China decreased by 39.5%, from 59.1 μg/m3 in 2013 to 35.8 μg/m3 in 2017. Subsequently, PM2.5 attributable premature deaths reduced 12.6%, from 1.20 million (95% CI: 0.57; 1.71) in 2013 to 1.05 million (95% CI: 0.44; 1.44) in 2017. This declining trend was found in most parts of China except some areas in Xinjiang, Jilin, and Heilongjiang provinces. As a result, 214,821 (95% CI: 96,675; 302,897) life were saved with an estimated monetary value of US$ 210.14 billion (2011 values). However, it has to be acknowledged that, the central and northern China within priority areas of air pollution control were still experiencing high numbers of premature deaths due to the severe PM2.5 pollution and high-density population. But more worrying than these priority areas are those Harbin-Changchun Metropolitan Region, City Belt in Central Henan and Yangtze-Huaihe City Belt in non-priority areas, which also have been seriously suffering PM2.5 attributable premature deaths over 28, 000 cases per year. In conclusion, despite the huge gain in life-saving effects in China over the past five years with the help of air pollution intervention policy, future work on cleaner air and better human health is still strongly needed, especially in non-priority areas of air pollution control.

The abrupt alternation between hot and wet extremes can lead to more severe societal impacts than isolated extremes. However, despite an understanding of hot and wet extremes separately, their temporally compounding characteristics are not well examined yet. Our study presents a comprehensive assessment of successive heat‐pluvial and pluvial‐heat events globally. We find that these successive extremes within a week occur every 6–7 years on average within warm seasons during 1956–2015, about 15% more often than would be expected by chance, and that they have a significant increase in frequency of about 22% per decade due to warming. We further investigate the role of vapor pressure deficit (VPD) and find that heat‐pluvial (pluvial‐heat) events are linked to negative (positive) VPD anomalies. Our results are statistically significant based on moving‐blocks bootstrap resampling and field significance tests, highlighting these methods' importance in robustly identifying compound events under autocorrelation and multiple‐testing conditions.

Heatwaves challenge electricity systems relying on wind energy for renewable power. However, the global availability of wind power during heatwaves remains unknown. Here, we provide a global assessment of wind power dynamics during heatwaves using weather reanalysis data from 1980 to 2023. Our results show the global land area experiencing wind power shortages during heatwaves has risen by 6.3% per decade, reaching 60% by 2023. Australia, Northern Asia, and Europe are particularly vulnerable, with wind power decreasing by 30–50%. Conversely, regions such as the Amazon, the Great Plains in North America, and Central Africa experience slight increases in wind power during heatwaves. 75.5% of land areas show a negative response between maximum temperatures and wind speeds. Our findings highlight the growing risk of concurrent heatwaves and wind power shortages, underscoring the need for targeted regional strategies to ensure the reliability of wind energy systems under a warming climate.

Reanalysis datasets, such as ERA5, are essential for climate research, offering comprehensive spatiotemporal coverage. However, their accuracy needs thorough evaluation for effective regional application, particularly in areas with complex topography like the Poyang Lake Basin (PLB), China’s largest freshwater lake. This study evaluated ERA5’s accuracy in simulating near-surface air temperature and precipitation in the PLB, using data from 24 meteorological stations. Key metrics, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) were applied across daily, monthly, seasonal, and annual scales. The results show that ERA5 performs well for daily mean temperature, with daily R values above 0.98 and RMSEs ranging from 0.95 °C to 3.11 °C. Its highest accuracy was in February and March, with R values exceeding 0.95, and seasonal trends were best captured in spring and autumn (R > 0.99). However, ERA5’s performance for precipitation was less accurate, with daily R values between 0.578 and 0.687 and RMSEs between 8.58 mm and 11.10 mm. ERA5 consistently overestimated precipitation, particularly during 1980–2003. These findings highlight ERA5’s strengths in temperature modeling and its limitations in precipitation, providing insights for identifying climate events and improving climate simulation in the PLB.