EGUsphere EGUsphere EGUsphere EGUsphere Copernicus Publications Göttingen, Germany 10.5194/egusphere-2025-2438 Non-Stationary Dynamics of Compound Climate Extremes: A WRF-CMIP6-GAMLSS Framework for Risk Reassessment in Southeastern China Zhang Yinchi https://orcid.org/0009-0003-1751-5959 1 2 3 Xu Wanling 4 Deng Chao 5 Sun Shao 6 Ma Miaomiao 7 Wei Jianhui https://orcid.org/0000-0001-8609-9600 8 Chen Ying 1 2 3 9 Kunstmann Harald 8 Gao Lu https://orcid.org/0000-0003-1934-5802 1 2 3 9 Key Laboratory for Humid Subtropical Eco-geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, 350117, China Institute of Geography, Fujian Normal University, Fuzhou, 350117, China School of Geographical Science, Fujian Normal University, Fuzhou, 350117, China School of Ocean and Earth Science, Tongji University, Shanghai, 200092, China School of Geography and Ecotourism, Southwest Forestry University, Kunming 650224, China State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China China Institute of Water Resources and Hydropower Research, Beijing, 100038, China Institute of Meteorology and Climate Research (IMKIFU), Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany Fujian Provincial Engineering Research Center for Monitoring and Accessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China 16 06 2025 2025 1 33 Copyright: © 2025 Yinchi Zhang et al. 2025 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2438/ The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2438/egusphere-2025-2438.pdf

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.