Spatiotemporal Evolution and Nonlinear Effects of Urban Morphology on Land Surface Temperature in the Context of Heatwaves

Frequent extreme heatwaves (HWs) have significantly exacerbated urban thermal risks, yet the regulatory mechanisms of urban morphology remain poorly understood. This study focuses on the core urban areas of Beijing and develops a Local Climate Zone (LCZ)-constrained spatiotemporal data fusion model (LCZ-FSDAF) to generate high-resolution Land Surface Temperature (LST) datasets from 2015 to 2024. By integrating urban–rural gradient analysis with the XGBoost-SHAP model, this study quantitatively resolves the spatiotemporal evolution of land surface temperature during heatwaves and the nonlinear threshold effects of urban morphological parameters, using a representative extreme heatwave event in July 2023 as a case study. The results indicate that the LCZ-FSDAF model achieves high precision across complex urban underlying surfaces (up to 0.946, RMSE as low as 0.762 K), effectively capturing the spatial heterogeneity of the urban thermal environment. Over the past decade, heatwave events in Beijing have exhibited a significant trend of increasing frequency, duration, and intensity. During these events, LST displays a concentric core-high, periphery-low structure; however, the peak temperature shifts toward high-density built-up areas in the sub-core, manifesting a distinct heat island core shift phenomenon. Furthermore, the impact of urban morphology on LST is characterized by significant nonlinearity, with the Normalized Difference Vegetation Index (NDVI) and Mean Building Height (MBH) identified as dominant factors. Notably, Building Coverage (BC) and Sky View Factor (SVF) exhibit pronounced threshold effects across different thermal indicators. Findings of this study are useful for guiding urban planning, optimizing spatial configurations, formulating urban heat island mitigation policies under heatwaves, and promoting the Sustainable Development Goals (SDGs) of cities and communities.