Legasa et al. (2025) Strengths and Limitations of Statistical and Dynamical Downscaling for the Representation of Compound Dry and Hot Events Over Spain

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Climatology
• Year: 2025
• Date: 2025-11-14
• Authors: Mikel N. Legasa, Ana Casanueva, Rodrigo Manzanas
• DOI: 10.1002/joc.70183

Research Groups

Not explicitly available in the provided abstract.

Short Summary

This study evaluates the performance of statistical and dynamical downscaling approaches in reproducing compound dry-hot events over Spain and the Balearic Islands. It finds that while both approaches perform well for individual variables, their performance declines for compound extremes, with neither consistently outperforming the other, highlighting the need for more advanced model development.

Objective

• To evaluate the performance of statistical and dynamical downscaling approaches in reproducing compound dry-hot events (co-occurring high temperatures and low precipitation), as represented by the standardised dry and hot index (SDHI).

Study Configuration

• Spatial Scale: Mainland Spain and the Balearic Islands.
• Temporal Scale: Not explicitly defined in the abstract, but implied for historical evaluation of downscaling methods.

Methodology and Data

• Models used:
  • Statistical Downscaling (SD): Generalised linear models, a posteriori random forests, convolutional neural networks.
  • Dynamical Downscaling (DD): Three EURO-CORDEX regional climate models (RCMs).
• Data sources: Implied observational data for evaluation of downscaling methods.

Main Results

• All models (statistical and dynamical) provide good results for downscaling individual precipitation and temperature and are capable of capturing standard multivariate metrics (e.g., Spearman correlation).
• Performance declines significantly when reproducing compound dry-hot events.
• Neither statistical nor dynamical downscaling consistently outperforms the other for compound extremes.
• Statistical downscaling methods outperform regional climate models in reproducing the observed temporal variability of compound dry-hot events.
• Regional climate models are better at simulating the intensity of these events, likely due to their foundation in physical processes.
• Both approaches show limitations in properly capturing the tails (dry and hot) of the multivariate distribution.

Contributions

• Provides a comparative evaluation of statistical versus dynamical downscaling methods specifically for the reproduction of compound dry-hot events.
• Identifies specific strengths and weaknesses of each downscaling approach (temporal variability vs. intensity) in the context of compound extremes.
• Highlights the critical need for advanced model development to accurately analyze compound events at local scales for practical applications.

Funding

Not available in the provided abstract.

Citation

@article{Legasa2025Strengths,
  author = {Legasa, Mikel N. and Casanueva, Ana and Manzanas, Rodrigo},
  title = {Strengths and Limitations of Statistical and Dynamical Downscaling for the Representation of Compound Dry and Hot Events Over Spain},
  journal = {International Journal of Climatology},
  year = {2025},
  doi = {10.1002/joc.70183},
  url = {https://doi.org/10.1002/joc.70183}
}