ABSTRACT Understanding the relationship between air temperature ( T a ) and surface temperature ( T s ) is crucial for urban climate studies, as it aids sustainable urban development and climate adaptation. Given the critical role of urban morphology in climate dynamics, this study examines how urban configurations impact temperature variations as well as provides evidence for future development and mitigating heat‐related risks. We examined the relationship between T a and T s through the lens of urban configuration, considering land use (LU) and local climate zones (LCZs) for the densely populated city of Hong Kong. Additionally, multiple linear regression (MLR) and random forest (RF) models were developed to predict spatially continuous T a using T s , LU, LCZ and time of day. Our analysis revealed a dynamic relationship between thermal dynamics and both time of day as well as urban configurations, with a stronger relationship between T a and T s in the daytime than at night. Furthermore, the difference between T a and T s showed a stronger correlation with LCZ than with LU classes. Model evaluation demonstrated the superior performance of RF over MLR in predicting spatially continuous T a ( R 2 = 0.80 vs. 0.75). The results from SHapley Additive exPlanations (SHAP) model analysis of RF prediction further emphasised the substantial contribution of residential LU and compact high‐rise LCZ in continuous T a prediction in the study area. These findings offer valuable insights for urban planning in densely populated cities, aiding in the formulation of development plans that promote stable urban thermal comfort.
Adeniran et al. (Thu,) studied this question.