Subseasonal Predictability of the North China Heat Extremes in Dynamical Models

North China, a region with climatologically temperate summers, has experienced a pronounced increase in extreme heat events under global warming, amplifying risks to societal resilience and public health. However, the predictability of these extremes, particularly sources of prediction errors, remains inadequately quantified. This study investigates prediction skill and potential sources of prediction errors for North China summer heat extremes using hindcasts and real-time forecasts from the Beijing Climate Center (BCC) and ECMWF subseasonal to seasonal (S2S) prediction systems. Our analysis reveals that effective prediction for North China summer heat extremes is limited to approximately 2–3 pentads, with ECMWF consistently outperforming BCC in both deterministic and probabilistic forecasts. The primary constraint on prediction skill stems from model performance in forecasting Eurasian mid–high latitude atmospheric circulation patterns, which largely explains ECMWF’s superior performance. While both systems reproduce observed relationships between North China heat extremes and local anticyclonic anomalies, ECMWF maintains significant prediction skill for critical mid–high latitude circulation features at 3-pentad leads, whereas BCC’s skill becomes confined to low-latitude regions. Real-time forecasts of two distinct 2024 heat events further validate this circulation-dependent predictability. These findings underscore that advancing heat extreme prediction requires targeted improvements in large-scale circulation forecasting, providing prioritized development pathways for S2S models. Notably, probabilistic forecasts provided meaningful early risk indicators even when deterministic thresholds were unmet, offering actionable technical approaches to enhance operational early warning capabilities.