Articles | Volume 22, issue 8
Research article
07 Aug 2018
Research article |  | 07 Aug 2018

A classification algorithm for selective dynamical downscaling of precipitation extremes

Edmund P. Meredith, Henning W. Rust, and Uwe Ulbrich

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Subject: Hydrometeorology | Techniques and Approaches: Stochastic approaches
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Cited articles

Ban, N., Schmidli, J., and Schär, C.: Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations, J. Geophys. Res.-Atmos., 119, 7889–7907, 2014. a, b
Bárdossy, A.: Atmospheric circulation pattern classification for South-West Germany using hydrological variables, Phys. Chem. Earth A/B/C, 35, 498–506, 2010. a
Barnes, E. A. and Polvani, L.: Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models, J. Climate, 26, 7117–7135, 2013. a, b
Benestad, R. E., Hanssen-Bauer, I., and Chen, D.: Empirical-statistical downscaling, World Scientific Publishing Company, Singapore,, 2008. a
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723,, 2017. a
Short summary
Kilometre-scale climate-model data are of great benefit to both hydrologists and end users studying extreme precipitation, though often unavailable due to the computational expense associated with such high-resolution simulations. We develop a method which identifies days with enhanced risk of extreme rainfall over a catchment, so that high-resolution simulations can be performed only when such a risk exists, reducing computational expense by over 90 % while still well capturing the extremes.