Increasing heatwave intensity and mortality demand timely and accurate heatwave prediction. The present study focused on teleconnection, the influence of distant land and ocean variability on local weather events, to drive long-term heatwave predictions. The complexity of teleconnection poses challenges for physical-based prediction models. In this study, we employed a machine learning model and explainable artificial intelligence to identify the teleconnection drivers for heatwaves in South Korea. Drivers were selected based on their statistical significance with annual heatwave frequency ( | R | > 0.3, p < 0.05). Our analysis revealed that two snow depth (SD) variabilities—a decrease in the Gobi Desert and increase in the Tianshan Mountains—are the most important and predictive teleconnection drivers. These drivers exhibit a high correlation with summer climate conditions conducive to heatwaves. Our study lays the groundwork for further research into understanding land–atmosphere interactions over these two SD regions and their significant impact on heatwave patterns in South Korea.

不断增加的热浪强度和死亡率需要及时、准确的热浪预测。目前的研究重点是遥相关，即遥远的陆地和海洋变化对当地天气事件的影响，以推动长期热浪预测。远程连接的复杂性给基于物理的预测模型带来了挑战。在这项研究中，我们采用机器学习模型和可解释的人工智能来识别韩国热浪的远程连接驱动因素。驾驶员的选择基于其与年度热浪频率的统计显着性（| R | > 0.3， p < 0.05）。我们的分析表明，两个雪深（SD）变化——戈壁沙漠的减少和天山山脉的增加——是最重要和最具预测性的遥相关驱动因素。这些驱动因素与有利于热浪的夏季气候条件具有高度相关性。我们的研究为进一步研究了解这两个 SD 地区的陆地与大气相互作用及其对韩国热浪模式的重大影响奠定了基础。