Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability.

翻译：热浪（HWs）是产生重大社会与环境影响的极端大气事件。预测此类极端事件仍然具有挑战性，因为它们与大尺度大气和气候变量之间的复杂相互作用难以通过传统统计和动力学模型捕捉。本研究提出了一种用于极端气候事件驱动因素识别的通用方法。通过将聚类算法与集成进化算法相结合，我们提出了一种新颖的框架（STCO-FS），以识别关键即时（短期）热浪驱动因素。该框架分析时空数据，通过对每个变量中相似的地理节点进行分组来降维，并在空间和时间域中开发驱动因素选择方法，从而识别预测变量与热浪发生之间的最佳时间滞后。所提出的方法已应用于分析意大利阿达河流域的热浪事件。该方法有效识别了影响该区域热浪的重要变量。此项研究有望增进我们对热浪驱动因素及其可预测性的理解。