Projection of Hydrological Drought in Chinese River Basins Under Climate Change Scenarios and Analysis of the Contribution of Internal Climate Variability

This study focuses on 120 representative river basins across China, utilizing CMIP6 multi-model climate data and CESM2-LE large ensemble climate data to develop a bias-correction framework for climate models that integrates statistical methods, with the aim of enhancing the spatiotemporal accuracy of climate model outputs. Building on this framework, the study simulates the evolution of hydrological drought characteristics in Chinese river basins during 2071–2100 under the SSP370 scenario and quantifies the relative contributions of internal climate variability (ICV), anthropogenic climate change (ACC), and inter-model uncertainty (IMU) to hydrological drought projections. Results reveal a pronounced south–north divergence in future drought risk. Southern China—especially the middle–lower Yangtze and Pearl River basins—exhibits a >10% increase in drought frequency, with event totals exceeding 30 per 30 years, yet individual droughts remain short and moderate in intensity. Conversely, northern basins—particularly the Songliao and Liao River systems—display pronounced lengthening and intensification of droughts, with mean duration surpassing 12 months and severity indices rising above 38, translating to 20~40% increases relative to the 1985~2014 baseline. Nationwide, ICV emerges as the dominant driver of projected changes: signal-to-noise ratios for frequency, intensity, and duration fall below unity across more than 70% of basins, indicating that unforced variability overshadows the anthropogenic trend. ACC signals only exceed ICV in southeastern coastal regions and parts of the Pearl River basin for intensity and duration. Inter-model spread rivals or exceeds ICV uncertainty in these same humid subtropical basins, underscoring the sensitivity of projections to model structure.