Rocky desertification severely restricts socio-economic development in the karst regions. However, assessments linking karst rocky desertification and NPP changes over the long term and at high resolution are limited. This study aims to reveal the spatiotemporal patterns and driving mechanisms of NPP changes in Wenshan Prefecture, addressing the scientific gap in quantitative process research and mechanism identification in karst desertification areas. We estimated vegetation NPP from 2000 to 2020 using remote sensing data and the CASA model. The Theil–Sen trend analysis and Mann–Kendall test were applied to assess temporal variation, while a Geographical Detector identified the dominant natural and human factors and their interactions shaping NPP spatial patterns. Our results showed that NPP increased overall by 4.07 gC m⁻² a⁻¹, alongside a general decline in rocky desertification. The most significant improvement occurred between 2010 and 2015, when rocky desertification shrank by 2224 km² and the dynamic rate reached 1.42%. Mean NPP reached 1057 gC m⁻² a⁻¹, with a “northwest high–southeast low” spatial pattern, and 77% of the region showed significant increases. Rocky desertification was most severe at elevations between 1000 and 2000 m. In the karst region, NPP is mainly controlled by natural factors, with soil depth and slope being the strongest influences. Human activity had the largest negative impact, and most factors interacted synergistically, where hydrothermal gradients and human disturbances more strongly suppressed NPP on steep, thin slopes than individually expected. These findings provide robust scientific evidence and practical decision-making support for ecological restoration, rocky desertification control and long-term sustainable development in Wenshan and other karst regions, highlighting the importance of continuous monitoring and adaptive management strategies to consolidate restoration achievements and guide future land-use planning and regional ecological policy.

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.