Comment on egusphere-2025-2438

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<strong class="journal-contentHeaderColor">Abstract.</strong> Understanding future changes in compound climate extremes (CCEs) is critical for climate risk assessment. However, existing research have relied on stationary assumptions, overlooking the dynamic evolution of CCEs under non-stationary climate change. Therefore, based on an enhanced Generalized Additive Models for Location, Scale, and Shape (GAMLSS), this study provides novel perspectives into the non-stationary characteristics of hot-wet (HW), hot-dry (HD), cold-wet (CW), and cold-dry (CD) extremes under future climate scenarios, focusing on the Minjiang River Basin (MRB), located in Southeast China. The high-resolution dataset employed for CCEs detection is generated through dynamical downscaling of a bias-corrected CMIP6 dataset, utilizing the Weather Research and Forecasting (WRF) model. The results show that (1) CCEs increase significantly at a rate of 3.55d/10a under the SSP5-8.5 scenario, with hot extremes (HW and HD) playing a dominant role. The spatial distribution exhibits a distinct west to east increasing gradient, peaking in the MRB downstream areas. (2) Under the SSP5-8.5 scenario, CCEs exhibit a marked transition from stationary to non-stationary characteristics, with non-stationarity detected in 95.20 % of grid cells. Mean warming, not variability, served as the dominant factor behind this transition, explaining 80.81 % of the changes. (3) The non-stationary results demonstrate that the severity and recurrence risks of CCEs are systematically underestimated. Most CCEs (except for CD) exhibit increasing recurrence risks under the SSP5-8.5 scenario, with a trend of 3.12d/10a in the 100-year return period, showing a stronger increase. This study emphasizes the necessity of updating the risk changes of CCEs under a non-stationary framework.