The urban heat island (UHI) effect not only impacts urban climates and residents’ quality of life but also poses challenges to energy consumption and sustainable development in cities. While many studies have explored the relative importance and marginal effects of two-dimensional (2D)/ three-dimensional (3D) urban morphology on land surface temperature (LST) to mitigate UHI, the interactive effects of these 2D/3D morphology metrics on daytime and nighttime LST at different grid scales have been largely overlooked. This study focuses on the area within the outer ring of Tianjin, and analyzes the relative importance, marginal effects, and particularly the interaction effects of 2D/3D urban morphology on LST. Our findings reveal the following: (1) The normalized difference vegetation index (NDVI) has the most significant cooling effect across all variables and at all three grid scales. (2) NDVI and bare land coverage have the greatest impact on daytime LST, while building height and tree height (TH) predominantly influence nighttime LST. (3) The relationships between key 2D/3D metrics and LST are nonlinear. Overall, NDVI is negatively correlated with LST across all three grid scales. (4) Interactions between 2D/3D metrics affect LST; LST decreases when TH exceeds 1.8 m and building density is below 62%, or when TH is below 1.8 m and building density exceeds 62%. These findings provide valuable insights and recommendations for sustainable urban development and effective heat adaptation strategies in specific locations.

With accelerating urbanization and global climate warming, Urban Heat Islands (UHIs) pose serious threats to urban development. Existing UHI research mainly focuses on inland regions, lacking systematic understanding of coastal city heat island mechanisms. We selected eight Chinese coastal cities with different backgrounds, quantitatively assessed urban heat island intensity based on summer 2023 Landsat 8 remote sensing data, established block-LCZ spatial analysis units, and employed a combination of machine learning models and causal inference methods to systematically analyze the regional differentiation characteristics of Urban Heat Island Intensity (UHII) and the influence mechanisms of multi-dimensional driving factors within land–sea interaction contexts. The results revealed the following: (1) UHII in the study area presents obvious spatial differentiation, with the highest value occurring in Hong Kong (2.63 °C). Northern cities generally had higher values than southern ones. (2) Different Local Climate Zone (LCZ) types show significant differences in thermal contributions, with LCZ2 (compact midrise) blocks presenting the highest UHII values in most cities, while LCZ G (water) and LCZ A (dense trees) blocks exhibit stable cooling effects. Nighttime light (NTL) and distance to sea (DS) are dominant factors affecting UHII, with NTL marginal effect curves generally presenting hump-shaped characteristics, while DS shows different response patterns across cities. (3) Causal inference reveals true causal driving mechanisms beyond correlations, finding that causal effects of key factors exhibit significant spatial heterogeneity. The research findings provide a new cognitive framework for understanding the formation mechanisms of thermal environments in Chinese coastal cities and offer a quantitative basis for formulating regionalized UHI mitigation strategies.