Extreme heat events can induce delayed responses in seasonally frozen ground (SFG) by altering soil heat storage and release processes, yet their lag characteristics and spatial variability remain poorly quantified. Here, we develop a distributed lag nonlinear model integrated with an explainable artificial intelligence framework (DLNM-XAI) to quantify the lagged responses of SFG to extreme heat events across the Qinghai-Tibet Plateau (QTP), based on in situ observations, multi-source remote sensing, and reanalysis datasets. Results show that extreme heat significantly modifies freeze–thaw dynamics. On average, each additional extreme heat day advances the thaw end date by 3.02 days, delays the freeze onset date by 1.52 days, and reduces maximum freezing depth by approximately 1.12 cm. The response of freezing depth exhibits a clear nonlinear lag pattern, with effects emerging within 5-10 days, peaking after approximately 15-20 days, and gradually weakening thereafter. Spatially, lagged responses also show pronounced spatial heterogeneity across the QTP. Regions with deeper snow cover, higher soil moisture, and stronger surface energy exchange generally exhibit longer lag durations. These factors, including extreme heat duration, snow depth, soil moisture, and surface energy fluxes, jointly regulate soil heat transfer and energy retention, thereby modulating the timing and persistence of SFG responses. Overall, this study provides a regional-scale characterization of the delayed thermal responses of SFG to extreme heat events and improves understanding of thermal memory and land-atmosphere interactions under short-term extreme climate forcing in cold regions.