Articles | Volume 21, issue 3
https://doi.org/10.5194/hess-21-1693-2017
https://doi.org/10.5194/hess-21-1693-2017
Research article
 | 
22 Mar 2017
Research article |  | 22 Mar 2017

A combined statistical bias correction and stochastic downscaling method for precipitation

Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by Editor and Referees) (09 Jan 2017) by Luis Samaniego
AR by Claudia Volosciuk on behalf of the Authors (20 Feb 2017)  Author's response   Manuscript 
ED: Publish subject to minor revisions (further review by Editor) (21 Feb 2017) by Luis Samaniego
AR by Claudia Volosciuk on behalf of the Authors (03 Mar 2017)  Author's response   Manuscript 
ED: Publish as is (06 Mar 2017) by Luis Samaniego
AR by Claudia Volosciuk on behalf of the Authors (06 Mar 2017)
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Short summary
For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.