Urban thermal environments as interconnected systems: Emergent causal networks and dynamic synchronization

Urban heat is a growing concern, especially under global climate change and continuous urbanization. However, the understanding of its spatiotemporal propagation behaviors remains limited. In this study, we leverage a data-driven modelling framework that integrates causal inference, network topology analysis, and dynamic synchronization to investigate the structure and evolution of temperature-based causal networks across the continental United States. We perform the first systematic comparison of causal networks constructed using warm-season daytime and nighttime air temperature anomalies in urban and surrounding rural areas. Results suggest strong spatial coherence of network links, especially during nighttime, and small-world properties across all cases. In addition, urban heat dynamics becomes increasingly synchronized across cities over time, particularly for maximum air temperature. Different network centrality measures consistently identify the Great Lakes region as a key mediator for spreading and mediating heat perturbations. This system-level analysis provides new insights into the spatial organization and dynamic behaviors of urban heat in a changing climate.