ABSTRACT Compound wind‐precipitation extremes (CWPE) are among the most impactful compound climate extremes under climate change. Using three CWPE definitions (strict co‐occurrence, CWPE; temporal offset, CWPEdayₒffset; and spatial offset, CWPEₛpaceₒffset), we systematically analyse the spatiotemporal evolution of CWPE over China during 1980–2022. Based on multi‐model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we further evaluate historical simulations, project future changes under SSP1‐2. 6, SSP2‐4. 5, and SSP5‐8. 5, and quantify the sources of projection uncertainty. CWPE shows a pronounced “more in the east, less in the west” pattern, with hotspots in eastern Sichuan and coastal South China. Nationally, CWPE decreases during 1980–2010 but shifts to an increasing tendency during 2011–2022. Introducing temporal and spatial offsets markedly increases annual‐mean CWPE frequency and expands the affected area, with a stronger enhancement from spatial offsets. CWPE is strongly seasonal, occurring mainly in spring (MAM) and summer (JJA) and least in winter (DJF). CMIP6 models reproduce the large‐scale spatial pattern but generally overestimate CWPE magnitude, and biases are generally larger when offsets are considered. Projections indicate persistent increases in CWPE frequency under all scenarios, strongest under SSP5‐8. 5, with central and eastern China as the main hotspots and larger increases toward later decades and higher emissions. Uncertainty decomposition indicates that internal variability dominates projection uncertainty in the early period, but its relative contribution gradually weakens over time. In the mid‐to‐late period, model uncertainty becomes the dominant source and continues to increase, while scenario uncertainty remains the smallest contributor. This systematic assessment of CWPE helps inform climate‐risk management in China under climate change. Our results show that CWPE definitions strongly affect trend estimates and spatial identification; introducing spatiotemporal offsets increases the number of identified events and their spatial coverage, thereby providing a clearer signal of potential risk changes for climate‐impact assessment.
Zhao et al. (Tue,) studied this question.