The R-based climate4R open framework for reproducible climate data access and post-processing. - Maialen Iturbide; Joaquín Bedia; Sixto Herrera; Jorge Luis Baño-Medina; Jesús Fernández 001; María Dolores Frías; Rodrigo Manzanas; Daniel San Martín Segura; Ezequiel Cimadevilla Álvarez; Antonio S. Cofiño; José Manuel Gutiérrez Llorente (Environ. Model. Softw., 2019)

Understanding Deep Learning Decisions in Statistical Downscaling Models. - Jorge Baño-Medina (CI, 2020)

Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches. - Jose González-Abad; Jorge Baño-Medina; José Manuel Gutiérrez Llorente (CoRR, 2023)

On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling. - Jose González-Abad; Jorge Baño-Medina; Ignacio Heredia Cacha (CoRR, 2023)

Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions. - Jose González-Abad; Jorge Baño-Medina (CoRR, 2023)

Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications. - Jorge Luis Baño-Medina; Maialen Iturbide; Jesús Fernández 001; José Manuel Gutiérrez (CoRR, 2023)

Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric river. - Jorge Baño-Medina; Agniv Sengupta; Allison Michaelis; Luca Delle Monache; Julie Kalansky; Duncan Watson-Parris (CoRR, 2024)