Urban parks play a vital role in mitigating the urban heat island effect and enhancing urban climate resilience. However, quantitative characterization of park cooling effects and the synergistic mechanisms among multiple factors remains limited. Focusing on the central urban area of Nanjing, a typical high-density subtropical city, this study analyzes Landsat 8/9 imagery from 2022 to 2025. The inflection point method was used to quantify three core indicators—cooling intensity, cooling distance, and cooling efficiency—while Pearson correlation analysis was applied to identify key drivers and examine synergistic relationships. The results show that (1) urban parks exhibit a “central aggregation–peripheral diffusion” pattern, which corresponds to pronounced spatial variability in the thermal environment; (2) park cooling effects display strong spatiotemporal heterogeneity, with notable interannual fluctuations in cooling intensity and a relatively stable cooling distance of approximately 400–500 m; and (3) cooling performance is primarily governed by tri-factor synergy among park size, vegetation characteristics, and surrounding urban environmental conditions. Park size largely determines the cooling extent, whereas underlying surface properties and building density regulate or constrain cooling. These findings clarify quantitative patterns and composite drivers of park cooling in high-density cities and provide evidence to support climate-adaptive green space planning and urban heat mitigation strategies.

This study integrates the analysis of surface temperature data with natural and anthropogenic factors closely related to the urban thermal environment in Nanjing from 2000 to 2020, exploring the spatiotemporal variation characteristics of the urban heat island effect and the interactive relationships among its influencing factors. The research findings are as follows: (1) Between 2000 and 2020, the urban heat island effect in Nanjing exhibited an expansion trend radiating from the city center to the periphery, with the heat island phenomenon primarily concentrated in the old urban areas characterized by developed commerce, industry, and dense populations. Surface temperatures gradually decreased from the city center to the suburbs, forming a distinct spatial distribution gradient. Both the standard deviation ellipse and the centroid of high-temperature areas showed a southward shift. (2) Significant differences in surface temperatures were observed across different land use types, with built-up areas and arable land maintaining relatively stable and higher surface temperatures, while water bodies and forests exhibited lower and stable surface temperatures. (3) Vegetation coverage, normalized water body index, elevation, dispersion, and the Shannon diversity index were negatively correlated with surface temperature, while the normalized difference bare land index, building index, dispersion index, and patch cohesion index were positively correlated with surface temperature. In Nanjing, the interactive effects of dual factors on the urban heat island effect were found to be greater than those of individual factors, with vegetation coverage identified as the most critical factor affecting surface temperature. Considering multidimensional factors together enhances the understanding of the spatial patterns and causes of the urban heat island effect, clarifies the interrelationships and degrees of influence among natural, socio-economic, and landscape pattern factors, and provides a scientific basis for improving the quality of the living environment in Nanjing.