A model output statistic-based probabilistic approach for statistical downscaling of temperature
Author:
Funder
Department of Science and Technology, Government of India
Publisher
Springer Science and Business Media LLC
Subject
Atmospheric Science
Link
https://link.springer.com/content/pdf/10.1007/s00704-024-04860-7.pdf
Reference63 articles.
1. Alder RF, Gu G, Wang JJ, Huffman GJ, Curtis S, Bolvin D (2008) Relationships between global precipitation and surface temperature on interannual and longer timescales (1979–2006). J Geophys Res Atmos 113(22). https://doi.org/10.1029/2008JD010536
2. Basu B, Nogal M, O’Connor A (2020) New approach to multisite downscaling of precipitation by identifying different set of atmospheric predictor variables. J Hydrol Eng 25:04020013. https://doi.org/10.1061/(asce)he.1943-5584.0001900
3. Bedia J, Baño-Medina J, Legasa MN, Iturbide M, Manzanas R, Herrera S, Casanueva A, San-Martín D, Cofiño AS, Gutiérrez JM (2020) Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment. Geosci Model Dev 13:1711–1735. https://doi.org/10.5194/gmd-13-1711-2020
4. Ben Alaya MA, Chebana F, Ouarda TBMJ (2016) Multisite and multivariable statistical downscaling using a Gaussian copula quantile regression model. Clim Dyn 47:1383–1397. https://doi.org/10.1007/s00382-015-2908-3
5. Benestad RE (2011) A new global set of downscaled temperature scenarios. J Clim 24:2080–2098. https://doi.org/10.1175/2010JCLI3687.1
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