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

Supplement

https://doi.org/10.5194/gmd-17-8665-2024-supplement

Data sets

A spatially comprehensive, meteorological data set for Mexico, the U.S., and southern Canada (NCEI Accession 0129374) Ben Livneh et al. https://doi.org/10.7289/v5x34vf6

NOAA's nClimGrid-Daily Version 1 - Daily gridded temperature and precipitation for the Contiguous United States since 1951 I. Durre et al. https://doi.org/10.25921/c4gt-r169

CMIP6 ESGF LLNL Metagrid https://aims2.llnl.gov/search/cmip6

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

The NA-CORDEX dataset L. Mearns et al. https://doi.org/10.5065/D6SJ1JCH

LOCA2 D. W. Pierce https://cirrus.ucsd.edu/~pierce/LOCA2

DRCDP ESGF LLNL Metagrid https://aims2.llnl.gov/search/drcdp

Model code and software

Evaluating statistical downscaled products with expected hydroclimatic co-variances Seung H. Baek https://doi.org/10.5281/zenodo.11194306

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Executive editor
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.
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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.
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