Abstract

Compared with individual heatwaves or storm events, the compound extreme precipitations preconditioned by heatwaves (CHEPs) usually amplify their adverse repercussions on both ecosystems and society. However, little is known about the physical mechanisms of generations, especially considering precipitation types triggered by various factors and synoptic patterns. By classifying extreme precipitations based on duration, we conduct an event-based analysis and comprehensively assess CHEPs using the machine learning-constrained framework and binning scaling methods over China. We find the fraction of CHEPs to total extreme short-duration/long-duration precipitations (ESDPs/ELDPs) has substantially increased by 18%/15% from 1979 to 2021, when using dry-bulb temperature to identify heatwaves. More notably, the hotspots of CHEPs are generally consistent with those of ESDPs. The ESDPs play a dominant role in shaping CHEPs episodes, which are governed by enhancing atmospheric instability due to preconditioned heatwaves. The horizontal moisture advection and transient vertical dynamic motion of moisture, which are paramount to LDPs, is not significantly enhanced by the overheating atmosphere, leading to a small fraction of LDPs to CHEPs. In addition, the intensity of ESDPs tends to increase with air temperature at higher rates than that of ELDPs. As short-duration storms may trigger severe flash floods, ample attention should be paid to the escalating risks of CHEPs under climate change.

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