With the acceleration of global climate change and urbanization, urban resilience has become a critical issue. This study, based on the Pressure-State-Response (PSR) model, constructs an urban resilience evaluation index system for Sanming City. Indicator weights are determined by combining the Analytic Hierarchy Process (AHP) and the entropy weight method. Spatial analysis methods, such as spatial autocorrelation, kernel density estimation, standard deviation ellipses, and geographic detectors, are employed to explore spatial–temporal analysis and driving factors of urban resilience. The results show the following: (1) from 2014 to 2022, Sanming’s urban resilience index initially increased and then declined; (2) the spatial distribution of urban resilience is uneven, with high-resilience areas concentrated in the city center and southeast, while the northwest is relatively low; (3) Local Moran’s I analysis confirms significant positive spatial autocorrelation, with regional differences gradually expanding; (4) geographic detector analysis reveals that NDVI, monthly maximum precipitation, nighttime light index, annual average PM2.5 concentration, and impervious surface ratio are key drivers of urban resilience; (5) factor interactions show nonlinear enhancement, with ecological and climatic–environmental factors interacting as key drivers of urban resilience changes.
Li et al. (Tue,) studied this question.