Abstract: During June—July 2023, North China (NC) experienced a record-breaking extreme high temperature (EHT) event with temperatures exceeding 40℃ 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. Using hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the China Meteorological Administration (CMA) within the Subseasonal-to-Seasonal (S2S) Prediction Project and diagnostic analysis of the local temperature budget, surface energy budget, and large-scale atmospheric circulation, this study evaluates the performance of ECMWF and CMA dynamic models in forecasting the spatial distribution and intensity of the three EHT processes, and further reveals the 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 time of 10—11, 12—14, and 3—6 d, respectively. However, both models underestimate the amplitude of SAT anomalies, especially during P3; (2) when the forecast lead time exceeds 15 d for P1 and P2, and 5 d 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 of the localized high-pressure anomalies over NC further induce biases in the forecast of local physical processes and SAT anomalies. During P1 and P3, the models' underestimation of both shortwave and longwave radiation leads to local negative temperature tendency biases, with diabatic heating being the primary contributor. In contrast, during P2, the underestimation of adiabatic heating is 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 a key to improving EHT prediction skills over NC.