Enhanced Three-Dimensional (3D) Drought Tracking for Future Migration Patterns in China Under CMIP6 Projections

Analyzing drought evolution requires dynamic three-dimensional methods to capture spatiotemporal continuity. Existing approaches oversimplify drought patch connectivity by relying on overlapping logic, thereby neglecting dynamic evolution. We propose a novel three-dimensional identification method incorporating spatial autocorrelation and anisotropy. Using the ERA5 dataset and the multi-model ensemble mean (MEM) of CMIP6, we investigate meteorological drought characteristics and migration patterns in China during 1961–2010 (historical) and 2031–2080 (future, SSP2-4.5/SSP5-8.5). Results indicate future drought frequency may decline by over 70% compared to historical levels, but severity, duration, affected area, and migration distance could increase significantly. Most future droughts (96.3% for SSP2-4.5; 95.0% for SSP5-8.5) are projected in spring and summer. Drought trajectories may predominantly shift northeastward (33% for SSP2-4.5; 38% for SSP5-8.5), with migration hotspots transitioning from the upper Yangtze River Basin to the upper Yellow River Basin. These findings enhance the understanding of drought dynamics and support the development of improved drought monitoring frameworks. The methodology and projections provide critical insights for drought risk management and adaptive water resource planning under climate change.