Air temperature (T_a) varies substantially within cities, yet physics-based models struggle to capture this fine-scale variability. We demonstrate that a graph-based approach – leveraging spatial information from sensor networks – can significantly enhance T_a forecasting at individual locations. Furthermore, we introduce an automated framework to streamline graph construction and model implementation. This study also demonstrates the distinct spatiotemporal dynamics of intra-city T_a patterns.