Subseasonal Prediction Skill of S2S Models for the Extreme Heat over North China in Summer 2023 and Its Sources of Predictability

　　
　　In June-July 2023, the North China (NC) experienced a record-breaking extreme high temperature (EHT) event, with temperatures exceeding 40°C at many meteorological stations. This event consisted of three relatively distinct processes, occurring during June 14–18 (P1), June 21–25 (P2), and June 30–July 3 (P3), respectively. This study investigates the local characteristics of these three EHT processes and the associated large-scale circulation systems, evaluates the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the China Meteorological Administration (CMA) dynamic models from the sub-seasonal to seasonal (S2S) prediction project in forecasting the spatial distribution and intensity of the three EHT processes, and further reveals the related sources of forecast errors and predictability. The results indicate that: (1) ECMWF and CMA models can predict the spatial distribution of surface air temperature (SAT) anomalies over NC for P1, P2 and P3 with lead times of 10–11 days, 12–14 days, and 3–6 days, respectively. However, both models underestimate the amplitude of SAT anomalies, especially during P3; (2) When the forecast lead time exceeds 15 days for P1 and P2, and 5 days for P3, both models fail to capture features of the mid-to-high-latitude Rossby wave train over Eurasia, resulting in prediction biases in both the location and intensity of localized high-pressure anomalies over NC; (3) Prediction biases in the localized high-pressure anomalies over NC further induce biases in forecasting local physical processes and SAT anomalies. During P1 and P3, the models’ underestimation of both shortwave and longwave radiation led to the local negative temperature tendency biases, with diabatic heating being the primary contributor. In contrast, during P2, the underestimation of adiabatic heating was primarily responsible for the cold bias in predicted SAT. This study highlights the mid-to-high-latitude subseasonal Rossby wave train teleconnection as a critical source of predictability for subseasonal EHT in NC, suggesting that better representation of this wave pattern is key to improving EHT prediction skills over NC.‌