Abstract Synchronous flooding across distant regions can amplify socioeconomic impacts. As the hydrological cycle intensifies, flood synchronization appears to be expanding, yet predictive understanding beyond gauged basins remains limited. We analyze the global synchronization of annual peak runoff (a proxy for flooding) using gridded data sets and complex network approach, and evaluate 13 CMIP6 (Coupled Model Intercomparison Project Phase 6) models in reproducing spatial synchronization patterns. CMIP6 models reproduce major synchronization hotspots but systematically overestimate connectivity and local clustering (in 90% and 85% regions, respectively) while underestimating average link length (in 70% of regions), yielding overly connected networks disproportionately dominated by short-range synchronization. We show that exaggerated temporal dependence driven by overly concentrated and shifted peak timing is the primary source of this structural bias. These emergent errors suggest a potential overestimation of spatially compound flood risk and highlight deficiencies of climate models in reproducing global hydrological seasonality, particularly in snow-dominated regions.