Against the backdrop of China’s “Dual Carbon” strategy (peak carbon emissions and carbon neutrality), timber forests serve the dual function of wood supply and carbon sink enhancement. In this study, we employed the Kuenm package in R to optimize Maximum Entropy model (MaxEnt) parameters. Based on the distribution data of six timber tree species in Sichuan Province and 43 environmental factors, we utilized the MaxEnt outputs and ArcGIS 10.8 software to map the geographic distribution of the suitable habitats for these species from the present day into the future (2061–2080) under different climate scenarios (SSP126 and SSP585). Furthermore, we analyzed the migration trend of their future distribution centers. The model optimization significantly improved both fit and predictive performance, with AUC values ranging from 0.8552 to 0.9637 and TSS values ranging from 0.6289 to 0.84, indicating high predictive capability and stability of the model. Analysis of environmental factors, including altitude, precipitation, and temperature, revealed that altitude plays a dominant role in species distribution. Future climate scenario simulations indicated that climate change will significantly alter the distribution of suitable habitats for these timber tree species. The suitable areas for some species contracted, with changes being particularly pronounced under the SSP585 scenario, in which the high-suitability area for Phoebe zhennan is projected to increase from 12,788 km² to 20,004 km², whereas the high-suitability area for Eucalyptus robusta is expected to contract from 8706 km² to 7715 km². The migration distances of suitable habitats for timber tree species in Sichuan range from 5 km to 101 km southwestward under different climate scenarios, and these shifts are statistically significant (p < 0.01), with shifts in elevation and precipitation patterns, reflecting species-specific responses to climate change. This study aims to predict future suitable habitats of timber tree species in Sichuan, providing scientific support for forestry planning, forest quality improvement, and climate risk mitigation.

Global warming has increased the frequency of droughts, further complicating wildfire susceptibility assessment in topographically complex mountainous regions. However, the factors associated with wildfire occurrence and their nonlinear spatial responses under contrasting drought conditions remain poorly understood. To address this knowledge gap, we conducted a case study in a fire-prone mountainous region of southwestern China. Drought and non-drought periods were identified using the Standardized Precipitation Evapotranspiration Index (SPEI) and run theory. Using historical wildfire records from 2006 to 2020 and 16 wildfire drivers, we developed three machine-learning models and applied GeoShapley to quantify the contributions of key predictors and characterize their spatial dynamics under different drought conditions. The results showed that the Extreme Gradient Boosting (XGB) model achieved the best predictive performance (AUC = 0.85–0.91) and effectively captured the spatial patterns of wildfire susceptibility. Meteorological variables consistently emerged as the dominant controls on wildfire occurrence, although their relative importance and functional effects differed substantially between drought and non-drought conditions. GeoShapley analysis further revealed pronounced spatial heterogeneity in the effects of the major drivers, with these spatial patterns further modulated by drought conditions. In particular, during drought periods, the local geographical context amplified the spatial interaction effect of precipitation, resulting in a stronger risk-enhancing effect than during non-drought periods. By decomposing variable contributions into non-spatial main effects and spatially explicit interaction effects, this study reveals how the effects of wildfire drivers vary spatially under contrasting drought conditions and provides a robust framework for targeted wildfire mitigation in complex mountainous landscapes.