Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1711-2020
https://doi.org/10.5194/gmd-13-1711-2020
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

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Cited articles

Abaurrea, J. and Asín, J.: Forecasting local daily precipitation patterns in a climate change scenario, Clim. Res., 28, 183–197, https://doi.org/10.3354/cr028183, 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., https://doi.org/10.5194/gmd-2019-278, in review, 2019. a
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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, https://doi.org/10.1007/s10584-013-0787-3, 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, https://doi.org/10.1016/j.cliser.2017.04.001, 2018. a
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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.