Intensification of precipitation extremes in the three river headwaters region under global warming

• Published in: Theoretical and applied climatology Vol. 156; no. 8; p. 433
• Main Authors: Zhang, Yunkai, Du, Juan, Li, Kai, Zhou, Chun, Liu, Zhen, Wu, Biqiong, Cao, Hui, Zhou, Li, Ao, Tianqi
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.08.2025; Springer Nature B.V
• Subjects: Annual precipitation; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Bias; Climate change; Climatology; Daily precipitation; Datasets; Disaggregation; Earth and Environmental Science; Earth Sciences; Environmental risk; Extreme weather; Global warming; Headwaters; Heavy precipitation; Hydrologic data; Hydrology; Interannual variability; Intercomparison; Land use; Precipitation; Precipitation data; Precipitation days; Regions; Rivers; Sensitivity analysis; Surface temperature; Trends; Waste Water Technology; Water Management; Water Pollution Control; Tibetan Plateau; China; Germany
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-025-05668-9

The Three River Headwaters (TRH) region is highly sensitive to climate change and ecologically vulnerable. Understanding future changes in extreme precipitation under global warming is therefore essential. However, existing extreme precipitation projections for the TRH are subject to considerable uncertainties and lack quantitative assessments of their responses to warming. To address these gaps, we employ the Bias Correction and Spatial Disaggregation (BCSD) framework to process daily precipitation data from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The bias-corrected dataset is then used to accurately project nine extreme precipitation indices under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5), and to evaluate their sensitivity to global mean surface temperature (GMST) increases. The results show that bias correction significantly improves ISIMIP3b models’ performance in simulating both climatology and interannual variability. All indices except for consecutive dry days (CDD) exhibit increasing trends in the future, with more pronounced changes under higher emission scenarios, suggesting the intensity, magnitude, frequency, and duration of extreme precipitation events in the TRH region are projected to increase. Furthermore, precipitation extremes show the highest sensitivity under SSP1-2.6, with the number of heavy precipitation days (R10), annual total precipitation of heavy precipitation (R95pTOT), and annual total wet day precipitation (PRCPTOT) increasing by 29.91%, 20.61%, and 15.68% per 1 K GMST rise, respectively. These findings offer scientific support for formulating regional adaptation strategies and mitigating climate risks in the TRH region.