Articles | Volume 13, issue 3
Development and technical paper
01 Apr 2020
Development and technical paper |  | 01 Apr 2020

Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment

Joaquín Bedia, Jorge Baño-Medina, Mikel N. Legasa, Maialen Iturbide, Rodrigo Manzanas, Sixto Herrera, Ana Casanueva, Daniel San-Martín, Antonio S. Cofiño, and José Manuel Gutiérrez

Related authors

Refining Remote Sensing precipitation Datasets in the South Pacific: An Adaptive Multi-Method Approach for Calibrating the TRMM Product
Óscar Mirones, Joaquín Bedia, Sixto Herrera, Maialen Iturbide, and Jorge Baño Medina
EGUsphere,,, 2023
Short summary
An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets
Maialen Iturbide, José M. Gutiérrez, Lincoln M. Alves, Joaquín Bedia, Ruth Cerezo-Mota, Ezequiel Cimadevilla, Antonio S. Cofiño, Alejandro Di Luca, Sergio Henrique Faria, Irina V. Gorodetskaya, Mathias Hauser, Sixto Herrera, Kevin Hennessy, Helene T. Hewitt, Richard G. Jones, Svitlana Krakovska, Rodrigo Manzanas, Daniel Martínez-Castro, Gemma T. Narisma, Intan S. Nurhati, Izidine Pinto, Sonia I. Seneviratne, Bart van den Hurk, and Carolina S. Vera
Earth Syst. Sci. Data, 12, 2959–2970,,, 2020
Short summary
Assessing the predictability of fire occurrence and area burned across phytoclimatic regions in Spain
J. Bedia, S. Herrera, and J. M. Gutiérrez
Nat. Hazards Earth Syst. Sci., 14, 53–66,,, 2014

Related subject area

Climate and Earth system modeling
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814,,, 2024
Short summary
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731,,, 2024
Short summary
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685,,, 2024
Short summary
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532,,, 2024
Short summary
cfr (v2024.1.26): a Python package for climate field reconstruction
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431,,, 2024
Short summary

Cited articles

Abaurrea, J. and Asín, J.: Forecasting local daily precipitation patterns in a climate change scenario, Clim. Res., 28, 183–197,, 2005. a
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling, Geosci. Model Dev. Discuss.,, in review, 2019. a
Barsugli, J. J., Guentchev, G., Horton, R. M., Wood, A., Mearns, L. O., Liang, X.-Z., Winkler, J. A., Dixon, K., Hayhoe, K., Rood, R. B., Goddard, L., Ray, A., Buja, L., and Ammann, C.: The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections, Eos T. Am. Geophys. Un., 94, 424–425,, 2013. a
Bedia, J., Herrera, S., San-Martín, D., Koutsias, N., and Gutiérrez, J. M.: Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling, Climatic Change, 120, 229–247,, 2013. a
Bedia, J., Golding, N., Casanueva, A., Iturbide, M., Buontempo, C., and Gutiérrez, J.: Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe, Climate Services, 9, 101–110,, 2018. a
Short summary
We introduce downscaleR, an open-source tool for statistical downscaling (SD) of climate information, implementing the most popular approaches and state-of-the-art techniques. It makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for the development of complex and fully reproducible SD experiments.