Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3417-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-3417-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2026
Research article |
| 22 Jul 2026
| 22 Jul 2026
Deep Learning Emulation of Multivariate Climate Indices: A Case Study of the Fire Weather Index in the Iberian Peninsula
Óscar Mirones
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Joaquín Bedia
Dept. Matemática Aplicada y Ciencias de la Computación (MACC), Universidad de Cantabria, Santander, Spain
Grupo de Meteorología y Computación, Universidad de Cantabria, Unidad Asociada al CSIC, Santander, Spain
Pedro M. M. Soares
Instituto Dom Luiz (IDL) – Faculdade de Ciências da Universidade de Lisboa (FCUL), Campo Grande Edifício C8, Piso 3, 1749-016 Lisboa, Portugal
José M. Gutiérrez
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, CA, USA
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Short summary
Wildfire danger is usually measured with a formula needing weather data from a specific time of day, often missing in climate simulations. Scientists have relied on a rough substitute that can be inaccurate. We trained artificial intelligence models on Iberian Peninsula weather records to recreate the proper measure using only common daily data. This proved far more accurate than the usual substitute, even without rainfall data, easing wildfire risk assessment for climate research and planning.
Wildfire danger is usually measured with a formula needing weather data from a specific time of...
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