Satellite-Based Evidence of Shorter-Term Lagged Drought Driving High-Intensity Wildfires in Subtropical China

Climatic drought shapes wildfire regimes, yet the multi-timescale lagged responses of high-intensity wildfires to drought and their spatial heterogeneity remain unclear in subtropical monsoon forests. We integrated long-term MODIS satellite observations and monthly drought metrics (SPEI, VPD, VAP, and precipitation) across subtropical China during 2005–2024. We used Spearman rank correlation to identify multi-scale drought–wildfire relationships and constructed zero-inflated negative binomial generalized linear mixed models (ZINB-GLMM) to quantify multi-temporal drought, vegetation, and anthropogenic effects. Our results showed that 64.7% of 0.5° grid cells experienced high-intensity wildfires, with 77.8% of events occurring in the cold dry season (November–March). Concurrent dry conditions (low SPEI, VAP, precipitation) generally increased fire susceptibility, while atmospheric aridity (high VPD) showed spatially dipolar effects. The 1–3-month lagged model had the highest explanatory power (R² = 0.54), identifying antecedent moisture deficit as the dominant driver. Dense vegetation amplified drought–fire synergies, especially in southeastern and southwestern China. Model predictive performance was strong overall, but lower accuracy in northern areas suggests additional local factors modulate wildfire risk there. This study provides a satellite-based spatiotemporal modeling framework that may serve as a methodological reference for ecological informatics-based wildfire early warning and climate-adaptive management in subtropical and analogous monsoon regions.