Compound extremes of lethal heat stress-heavy precipitation events (CHPEs) seriously threaten social and ecological sustainability, while their evolution and effects at the global scale under climate warming remain unclear. Here we develop the global picture of projected changes in CHPEs under various scenarios and investigate their socioeconomic and ecosystem risks combining hazard, exposure, and vulnerability through the composite indicator approach. We find a high percentage of heat stress is followed by heavy precipitation, probably driven by atmospheric conditions. Global average frequency and intensity of CHPEs are projected to increase in the future under high-emission scenarios. Joint return periods of CHPEs are projected to decrease globally, predominantly driven by changes in heat stress extremes. In the long-term future, over half of the population, gross domestic product, and gross primary productivity may face high risk in most regions, with developed regions facing the highest risks under SSP5-8.5 and developing regions facing the highest risks under SSP3-7.0.

Flash drought is distinguished with seasonal drought by its characteristics of sudden onset and rapid intensification, and has attracted increasing concern due to the severe negative impacts on socioeconomic development and ecosystems. To accurate identify the onset time and propagation of flash drought is still a big challenge, as it is influenced by many factors (e.g., deficit in precipitation, increase in evapotranspiration and temperature, shortage in soil moisture, and etc.). This study aims to develop a probabilistic and multivariable flash drought identification method (PMFDI) from the perspective of considering the interaction effects from different driving factors on flash drought occurrence and propagation with a multivariable statistical tool. The PMFDI uses the weekly standardized precipitation evapotranspiration index and the standardized soil moisture index to describe the abnormally changes in multiple hydrometeorological variables over a short duration. The linearly multivariable index (LMI) is employed to describe the correlation relationship between multiple hydrometeorological variables and to identify flash droughts from the aspects of sudden onset and rapid intensification. We select the Loess Plateau (LP) in China to verify the efficiency of the developed PMFDI with the comparison to the other two recently proposed flash drought identification methods. The results indicate that PMFDI can identify high‐ and low‐ frequency regions similar to those identified by the other two methods and can generally recognize actual flash droughts. In addition, the PMFDI can capture the comprehensive impact of multiple hydrometeorological variables as well as their interaction on flash drought occurrence, which provides a useful perspective to understand the propagation processes of the actual flash droughts in the LP.

The propagation of meteorological droughts to soil droughts poses a substantial threat to water resources, agricultural production, and social systems. Understanding drought propagation process is crucial for early warning and mitigation, but mechanisms of the propagation from meteorological drought to soil drought, particularly at varying soil depths, remain insufficiently understood. Here, we employ the maximum correlation coefficient method and the random forest (RF) model to investigate the spatiotemporal patterns and drivers of propagation time (PT) from meteorological drought to soil drought at four different depths across China from 1980 to 2018. Our findings reveal consistently higher PT in northern China and lower PT in southern China across varying soil depths, with more pronounced spatial heterogeneity with increasing soil depth. Furthermore, we identify temperature and precipitation as determinants of spatial patterns of PT in surface and deeper soil layers, respectively. Additionally, precipitation emerges as the dominant factor influencing changes in PT between different soil layers. Our study highlights a discernible shift in PT drivers from temperature to precipitation as soil depth increases and the significant impact of precipitation on exacerbating spatial heterogeneity in PT. This study contributes to an enhanced comprehension of the propagation process from meteorological drought to soil drought at different depths, which can aid in establishing practical drought mitigation measures and early warning systems.