Spatially clustered global wildfire trends and their climate, fuel and human drivers (2001–2025): A spatio-temporal trend-cluster attribution approach

The spatio-temporal distribution of wildfire trends reveals spatially uneven changes in fire activity across the globe. Existing studies primarily rely on temporal models and climatic variables but ignore the spatial disparities of drivers. This study develops a spatio-temporal trend-cluster attribution (STCA) model to explore joint climate–fuel–human controls on global fire-trend patterns. STCA combines spatial trend clustering, optimal parameters-based geographical detector and grid-based temporal analysis to identify spatial hotspot and coldspot regions in burned-area trends and to attribute them to individual and interactive climate, fuel and human effects. The model is implemented on global burned area derived from the Moderate Resolution Imaging Spectroradiometer (MODIS, MCD64A1) for 2001–2025, with drivers of climate and fire weather, fuel and vegetation, and anthropogenic pressure. Globally, burned area declined by 1.28 % ± 0.35 % of its 2001–2025 mean per year, but the model identified strong regional disparities, including nine trend hotspot regions and six trend coldspot regions of burned-area trends. The spatio-temporal analysis demonstrates that fire weather dominates globally, whereas savanna fuel and anthropogenic pressure become comparably important within trend clusters. In addition, warming and drying are associated with increases in hotspots, while cooling and wetting are associated with declines in coldspots. Finally, climate drivers weaken in rising hotspots and fuel control strengthens in declining coldspots. STCA provides a scalable and reproducible approach for identifying climate–fuel–human drivers on spatially structured wildfire trends.