Detectable Anthropogenic Influence in Mean Precipitation of China
Abstract Detecting and attributing regional‐scale mean precipitation changes remains a challenging scientific problem. Due to significant spatiotemporal variability of precipitation changes and the limited ability of climate models to simulate these variations, attribution studies of China's mean precipitation changes remain scarce. We analyze China's long‐term precipitation changes using four observational data sets and CMIP6 simulations, with percentage precipitation anomaly as a key metric. Through optimal fingerprinting detection, we identify anthropogenic signals in China's mean precipitation changes. Results reveal an increasing trend in annual precipitation across most regions since the 1960s, which CMIP6 models generally capture, though large inter‐model discrepancies persist in simulating trends in southern China. Human influence on China's mean precipitation changes is detectable and separable from natural forcings. Anthropogenic signals are detected in three sub‐climatic regions: Northwest China, Northeast China, and Tibetan Plateau. Three‐signal analysis indicates that the increase in China's precipitation is primarily driven by greenhouse gas forcing.
Authors
- Xiu‐Qun Yang (ORCID: https://orcid.org/0000-0003-3716-9152)
- Heyang Song (ORCID: https://orcid.org/0000-0002-4192-5603)
- Xuebin Zhang (ORCID: https://orcid.org/0000-0002-7022-1744)
- Tao Wang
- Ying Sun (ORCID: https://orcid.org/0000-0003-2335-3485)
Institutions
- China Meteorological Administration (CN)
- University of Victoria (CA)
- Pacific Institute for Climate Solutions (CA)
- Chinese Academy of Meteorological Sciences (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Geophysical Research Letters
- Published
- 2025-06-16
- DOI
- https://doi.org/10.1029/2025gl114870
- Citations
- 1
- Primary Topic
- Climate variability and models
- Type
- article
- Field-Weighted Citation Impact
- 1.11
Funders
- National Natural Science Foundation of China
- China Meteorological Administration