Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection

作者：J. Pérez-Aracil, César Peláez-Rodríguez, Ronan McAdam, Antonello Squintu, Cosmin M. Marina, Eugenio Lorente-Ramos, Niklas Luther, Verónica Torralba, Enrico Scoccimarro, Leone Cavicchia, Matteo Giuliani, Eduardo Zorita, Felicitas Hansen, David Barriopedro, Ricardo García‐Herrera, Pedro Antonio Gutiérrez, Jürg Luterbacher, Elena Xoplaki, Andrea Castelletti, Sancho Salcedo‐Sanz · 发表于：Weather and Climate Extremes · 年份：2025 · DOI：10.1016/j.wace.2025.100792 · 被引用次数：6 · 研究领域：Meteorological Phenomena and Simulations、Climate variability and models、Air Quality Monitoring and Forecasting

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 named Spatio-Temporal Cluster-Optimized Feature Selection (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 grid cells 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.