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

Viewed

Total article views: 4,279 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,781 1,397 101 4,279 102 120
  • HTML: 2,781
  • PDF: 1,397
  • XML: 101
  • Total: 4,279
  • BibTeX: 102
  • EndNote: 120
Views and downloads (calculated since 15 Sep 2016)
Cumulative views and downloads (calculated since 15 Sep 2016)

Viewed (geographical distribution)

Total article views: 4,279 (including HTML, PDF, and XML) Thereof 4,113 with geography defined and 166 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jun 2024
Download
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.