The PROMES Regional Climate Model: Three Decades of Development and Contributions to Regional Climate Ensemble Research

Regional climate models (RCMs) have become established as essential dynamical downscaling tools, providing physically consistent, high-resolution climate information where global climate models (GCMs) are unable to resolve. Added value is obtained over heterogeneous vegetation, coastal, urban or orographically complex zones. Temperature, precipitation or wind benefit whenever fine-scale processes govern the climate signal, as with extreme events. CORDEX initiative (since 2009) has further consolidated RCM research with coordinated multi-model ensembles covering all continental regions, enabling systematic uncertainty quantification of regional climate projections. PROMES RCM has been an active scientific contributor across three decades to the modelling community ensemble, with operations discontinued around 2015. This retrospective narrative review synthesizes and documents PROMES’s development, performance across Europe, West Africa and South America, and assesses its scientific contributions. This includes climate change projection features, extreme events (droughts, heat waves, or Mediterranean tropical-like cyclones) or land surface–atmosphere studies. Its behaviour within the more relevant international multi-model ensembles (from PRUDENCE to EuroCORDEX), with structurally independent characteristics, represents a scientific asset for uncertainty characterization beyond the model’s individual results, offering a legacy argument for preserving RCM diversity in ensemble design strategies, as it is an essential and many times underappreciated key point of uncertainty analysis.

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Journal
Geosciences
Published
2026-10-16
DOI
https://doi.org/10.3390/geosciences16100423
Primary Topic
Climate variability and models
Type
article
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article

The PROMES Regional Climate Model: Three Decades of Development and Contributions to Regional Climate Ensemble Research

Enrique Sánchez, Manuel de Castro, Μiguel Angel Gaertner, Clemente Gallardo
Geosciences
Climate variability and models
article

The PROMES Regional Climate Model: Three Decades of Development and Contributions to Regional Climate Ensemble Research

Enrique Sánchez, Manuel de Castro, Μiguel Angel Gaertner, Clemente Gallardo
article en

Abstract

Regional climate models (RCMs) have become established as essential dynamical downscaling tools, providing physically consistent, high-resolution climate information where global climate models (GCMs) are unable to resolve. Added value is obtained over heterogeneous vegetation, coastal, urban or orographically complex zones. Temperature, precipitation or wind benefit whenever fine-scale processes govern the climate signal, as with extreme events. CORDEX initiative (since 2009) has further consolidated RCM research with coordinated multi-model ensembles covering all continental regions, enabling systematic uncertainty quantification of regional climate projections. PROMES RCM has been an active scientific contributor across three decades to the modelling community ensemble, with operations discontinued around 2015. This retrospective narrative review synthesizes and documents PROMES’s development, performance across Europe, West Africa and South America, and assesses its scientific contributions. This includes climate change projection features, extreme events (droughts, heat waves, or Mediterranean tropical-like cyclones) or land surface–atmosphere studies. Its behaviour within the more relevant international multi-model ensembles (from PRUDENCE to EuroCORDEX), with structurally independent characteristics, represents a scientific asset for uncertainty characterization beyond the model’s individual results, offering a legacy argument for preserving RCM diversity in ensemble design strategies, as it is an essential and many times underappreciated key point of uncertainty analysis.

GeosciencesVol. 16(10)
University of Castilla-La Mancha (ES)
Climate action
Openalex Percentile: Top 17%
Climate variability and models
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