Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 23
Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8665-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8665-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 23
Methods for assessment of models
 | Highlight paper
 | 
09 Dec 2024
Methods for assessment of models | Highlight paper |  | 09 Dec 2024

Evaluating downscaled products with expected hydroclimatic co-variances

Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee

Viewed

Total article views: 4,141 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,779 277 85 4,141 158 111 160
  • HTML: 3,779
  • PDF: 277
  • XML: 85
  • Total: 4,141
  • Supplement: 158
  • BibTeX: 111
  • EndNote: 160
Views and downloads (calculated since 05 Jun 2024)
Cumulative views and downloads (calculated since 05 Jun 2024)

Viewed (geographical distribution)

Total article views: 4,141 (including HTML, PDF, and XML) Thereof 3,998 with geography defined and 143 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Mar 2026
Download
Editorial statement
This paper addresses the conditions in which GCM and downscaled solutions diverge for targeted processes under historical and future climate conditions. Downscaling is a crucial part of making climate model outputs useable by the wider science and policy community. Understanding the properties and limitations of downscaling should hence be of interest far beyond the model development community.
Read more
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Read more
Share