Accurately quantifying the driving mechanisms of land surface temperature (LST) is fundamental to developing climate-resilient urban strategies. However, traditional linear models often fail to capture the complex nonlinear interactions and spatial non-stationarity inherent in urban thermal environments, especially when hindered by multicollinearity among morphological indicators. This study proposes a multi-scale spatial explainability attribution framework by integrating an XGBoost machine learning model with SHAP (SHapley Additive Explanations) to decipher the thermal dynamics of Changchun, a representative cold-region city in China. Utilizing a 500 m grid-based dataset, we incorporated 3D urban morphology (BVD), land cover (NDVI, NDWI), and socioeconomic factors. The results indicate that the XGBoost model achieves superior predictive performance (R2 = 0.694) compared to traditional OLS models. SHAP global attribution identified Building Volume Density (BVD) as the primary warming driver, as its three-dimensional volume creates “thermal traps” through radiation trapping and reduced ventilation. Notably, NDVI exhibits a significant nonlinear “cooling threshold effect” at 0.3, beyond which its mitigation efficiency stagnates or even reverses due to vegetation fragmentation and heat-induced physiological stress. Furthermore, spatial mapping reveals a distinct “sign reversal” in NDWI’s impact, reflecting the dualistic thermal regulation of water bodies across different urban–rural gradients. These findings suggest that urban thermal management strategies should shift from merely restricting 2D surface occupancy (e.g., Building Density) to a more sophisticated approach focused on precisely controlling 3D volume intensity (BVD). This study provides a “point-to-area” diagnostic tool supporting a transition to spatially targeted urban planning interventions.
Qu et al. (Fri,) studied this question.