Given the significant social and economic impact caused by heat waves, there is a pressing need to predict them with high accuracy and reliability. In this study, we analyzed the real-time forecast data from six models constituting the Subseasonal-to-Seasonal (S2S) prediction project, to elucidate the key mechanisms contributing to the prediction of the recent record-breaking Korean heat wave event in 2018. Weekly anomalies were first obtained by subtracting the 2017- 2020 mean values for both S2S model simulations and observations. By comparing four Korean heat-wave-related indices from S2S models to the observed data, we aimed to identify key climate processes affecting prediction accuracy. The results showed that superior performance at predicting the 2018 Korean heat wave was achieved when the model showed better prediction performance for the anomalous anticyclonic activity in the upper troposphere of Eastern Europe and the cyclonic circulation over the Western North Pacific (WNP) region compared to the observed data. Furthermore, the development of upper-tropospheric anticyclones in Eastern Europe was closely related to global warming and the occurrence of La Niña events. The anomalous cyclonic flow in the WNP region coincided with enhancements in Madden- Julian oscillation phases 4-6. Our results indicate that, for the accurate prediction of heat waves, such as the 2018 Korean heat wave, it is imperative for the S2S models to realistically reproduce the variabilities over the Eastern Europe and WNP regions.