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

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. • We propose a framework which identifies key short-term heatwave drivers. • The proposed method combines dimensionality reduction and evolutionary algorithms. • Drivers include climate variables with time lags up to 6 months before the event. • Dimensionality reduction in the spatial domain is considered. • Results at Adda river basin identify key drivers and influential time frames.

Authors

Institutions

Publication Details

Journal
Weather and Climate Extremes
Published
2025-07-26
DOI
https://doi.org/10.1016/j.wace.2025.100792
Citations
3
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
3.90

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Matteo Giuliani, Jürg Luterbacher, Eduardo Zorita, David Barriopedro et al.
3 citations
Weather and Climate Extremes
Meteorological Phenomena and Simulations
3.90
article

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

Matteo Giuliani, Jürg Luterbacher, Eduardo Zorita, David Barriopedro, Enrico Scoccimarro, Leone Cavicchia, Verónica Torralba, Ricardo García‐Herrera, Felicitas Hansen, Sancho Salcedo‐Sanz, Pedro Antonio Gutiérrez, Antonello Squintu, Elena Xoplaki, César Peláez-Rodríguez, Niklas Luther, Ronan McAdam, Eugenio Lorente-Ramos, Andrea Castelletti, Cosmin M. Marina
article en
3 citations

Abstract

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. • We propose a framework which identifies key short-term heatwave drivers. • The proposed method combines dimensionality reduction and evolutionary algorithms. • Drivers include climate variables with time lags up to 6 months before the event. • Dimensionality reduction in the spatial domain is considered. • Results at Adda river basin identify key drivers and influential time frames.

Weather and Climate ExtremesVol. 49
Universidad Complutense de Madrid (ES), Consejo Superior de Investigaciones Científicas (ES), Universidad de Alcalá (ES), Justus-Liebig-Universität Gießen (DE), Barcelona Supercomputing Center (ES), RFF-CMCC European Institute on Economics and the Environment (IT), CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT), Helmholtz-Zentrum Hereon (DE), Instituto de Geociencias (ES), University of Córdoba (ES), Politecnico di Milano (IT), Universitat Politècnica de Catalunya (ES)
Comunidad de Madrid, European Commission, Generalitat de Catalunya, Universidad de Alcalá, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, European Regional Development Fund, Agencia Estatal de Investigación
Climate action
Openalex Percentile: Top 6%
Meteorological Phenomena and Simulations
3.90
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.