Urban air quality and temperature are closely linked through coupled physical and chemical processes. However, most existing evidence relies on correlation-based associations lacking directionality, or process-based models whose causal pathways depend on model structure and parameterizations. Here we apply a nonlinear causal inference method to quantify directed coupling between near-surface air temperature and three major air pollutants (PM2.5, ozone, and NO2) across 481 U.S. urban areas using 14 years of daily data. We identify distinct diurnal and seasonal regimes of temperature–pollution coupling not captured by linear correlation. Temperature–PM2.5 and temperature–O3 coupling strengthens in summer, whereas temperature–NO2 coupling intensifies in winter. Ozone shows the most consistent causal structure among all pollutants, with temperature dominant in roughly 80% of urban areas in both seasons. PM2.5 exhibits balanced and spatially heterogeneous coupling, while NO2 shifts from mixed behavior in summer to pronounced temperature dominance in winter. Across pollutants, linear correlations frequently overestimate coupling strength, especially for winter NO2. As the first continental-scale causal assessment of urban temperature–pollution interactions in the U.S., this study offers a data-driven complement to process-based modeling. The identified pollutant-specific sensitivities and their regional, diurnal, and seasonal variability provide new insight for understanding and managing urban heat stress and air quality.

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