Non-stationary dynamics of compound climate extremes: a WRF-CMIP6-GAMLSS framework for southeastern China

Zhang, Yinchi; Xu, Wanling; Deng, Chao; Sun, Shao; Ma, Miaomiao; Wei, Jianhui; Chen, Ying; Wang, Yi; Gao, Lu; Kunstmann, Harald

doi:10.5194/nhess-26-2031-2026

Articles | Volume 26, issue 5

https://doi.org/10.5194/nhess-26-2031-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-26-2031-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 5

Research article

|

08 May 2026

Research article |

| 08 May 2026

Non-stationary dynamics of compound climate extremes: a WRF-CMIP6-GAMLSS framework for southeastern China

Yinchi Zhang, Wanling Xu, Chao Deng, Shao Sun, Miaomiao Ma, Jianhui Wei, Ying Chen, Yi Wang, Lu Gao, and Harald Kunstmann

Supplement

https://doi.org/10.5194/nhess-26-2031-2026-supplement

Data sets

Bias-corrected CMIP6 global dataset Zhongfeng Xu https://www.scidb.cn/en/detail?dataSetId=791587189614968832&version=V4

the fifth generation ECMWF reanalysis European Centre for Medium-Range Weather Forecasts (ECMWF) https://cds.climate.copernicus.eu/cdsapp#!/dataset

Model code and software

GAMLSS code R. A. Rigby and D. M. Stasinopoulos https://github.com/gamlss-dev/gamlss

WRF code National Center for Atmospheric Research (NCAR) https://www2.mmm.ucar.edu/wrf/OnLineTutorial/Compile/index.php

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Short summary

Most studies of compound extremes assume stationary climate conditions. Here, we combine high-resolution regional climate modeling with non-stationary statistical methods to assess future changes in compound extremes over southeastern China. Our results demonstrate that non-stationary models more accurately capture shifts in the evolution of these events, suggesting that conventional approaches may systematically underestimate future risks.