The urban heat island (UHI) effect has a serious negative impact on urban ecosystems and human well-being. Mitigating UHI through nature-based methods is highly recommended. The cooling effect of urban blue infrastructure (UBI) can significantly alleviate the effects of UHI. Revealing the crucial influencing factors of the cooling effect of UBI is of great significance for mitigating the UHI effect. In this study, the water-cooling intensity (WCI) and water-cooling range (WCR) were used to quantitatively analyze the cooling effect of UBI in Hefei city in summer. Furthermore, the influencing factors and their interactions with the cooling effect of UBI were investigated based on the Geodetector model. The results revealed that: (1) The surface thermal environment of the built-up area of Hefei presented obvious spatial differentiation characteristics. (2) There were nine influencing factors that significantly influenced the WCI variation, with the greatest influencing factor of road density. In contrast, only the landscape shape index had a significant effect on WCR variation. (3) The interaction of environmental characteristics, water body characteristics, and socioeconomic characteristics had a significant influence on the cooling effect of UBI, and the interaction relationship between the influencing factors was mutually enhanced. The findings from our research can provide a theoretical reference and practical guidance for the protection, restoration, and planning of UBI as a nature-based solution to improve the urban thermal environment.

In this study, a retrospective analysis of the relationship between the land use/land cover (LULC) change and associated surface urban heat island (SUHI) effect in fast-growing Greater Hefei between 1995 and 2016 was performed. Our results reveal the heterogeneous patterns of LULC change. The concentric buffer-based urban–rural gradient analysis reveals that most of the newly emerging developed land occurred within downtown Hefei. In contrast, in three suburban municipality/county jurisdictions, the overall area change in the non-developed land was much lower, but the net increase in developed land is remarkable. Meanwhile, the spatiotemporal patterns of SUHI are in good agreement with that of the developed land, as evidenced by the notable increase in SUHI intensity (SUHII) levels and SUHI spatial extent (SUHISE) in response to the rapid urban expansion, particularly along transportation corridors. In addition, partial least square regression (PLSR) models indicate that the buffer-based predictors/independent variables are significantly related to the responses (SUHII and SUHISE), explaining approximately 61.3% of the variance in the SUHII and 79.8% of the variance in the SUHISE, respectively. Furthermore, the relative strength of the independent variables in determining the relationship was quantitatively examined. The findings of this study provide clear evidence for decision making for sustainable land development and mitigation of the SUHI effect.