Quantifying the dynamical linkage, co‐evolution, and propagation of regional heatwaves is essential to minimize socio‐economic losses. Here, we investigate such network structure and propagation characteristics for warm period (May–September) heatwaves over Conterminous United States using a complex network approach based on daily maximum temperature. The concept of Event Synchronization (ES) is applied to identify the source and sink regions primarily responsible for heatwave propagations and the strength of association between these regions. The network coefficients are derived to evaluate the extremal dependence, co‐evolution, and spatial propagation of large scale heatwavc events. The topology and propagation of heatwaves are influenced by the spatial distribution of the zonal and meridional air mass transport. Furthermore, we demonstrated the application of ES metrics and the network coefficients for heatwave days prediction between source and sink regions with true positive rate of 63% at a lead time of 2 days. Plain Language Summary The large scale heatwave events have become very common over the Conterminous United States. The examples include heatwave events in Chicago and the Gulf coastal plains (2019 and 2020) and the recent ongoing extreme heat event in California (August 2020). The United States incurs significant socio‐economic losses and health problems due to exposure to heatwaves. Under climate change, such heatwaves are likely to increase in different parts of the world, leading to increased socio‐economic impacts. We apply complex network analysis to understand the USA heatwaves’ regional connectivity and the underlying physical mechanisms. The derived information is essential in the forecasting of heatwaves. Key Points Dominant air mass transport and topographic characteristics influence the synchronization structure and propagation patterns of heatwaves Event Synchronization metrics can identify the source and sink regions primarily responsible for heatwave propagations Network coefficients able to capture the spatial dependency between warm‐period heatwave events occurring at different locations