While sea surface temperature anomalies (SSTAs) are known drivers of terrestrial heat extremes, their impacts across seasons, ocean basins, and lead times lack a unified quantitative understanding. Here, we apply complex network theory to systematically map the spatiotemporal pathways linking global SSTAs to extreme terrestrial heat. We show that the dominant ocean basins driving heat extremes vary seasonally, with signal flow analysis revealing distinct propagation pathways. The persistence of SSTA influences exhibits clear basin dependence: the Indian Ocean exerts the most rapid impact, followed by the Pacific and Atlantic. Notably, robust teleconnections are detectable over 90% of land areas even at 12-month lead times, exposing a substantial source of long-term predictability that conventional numerical models overlook. Coupled Model Intercomparison Project Phase 6 (CMIP6) models demonstrate skillful simulation of the lag-dependent evolution of these teleconnections, with a clear inter-model relationship showing that greater decadal variance in simulated ocean basins corresponds to higher numbers of network links. This network-based framework provides a novel approach for elucidating ocean-to-land interactions, offering critical insights that extend predictability beyond the limits of conventional numerical models.

Complex Network, Sea Surface Temperature Anomalies, Extreme Terrestrial Heat