Data · dataset · 2026
Evaluation of some distributional downscaling methods as applied to daily precipitation with an eye towards extremes
Listed in National Center for Atmospheric Research
Statistical downscaling (SD) methods used to refine future climate change projections produced by physical models have been applied to a variety of variables.
Description
We evaluate four empirical distributional type SD methods as applied to daily precipitation, which because of its binary nature (wet vs. dry days) and tendency for a long right tail presents a special challenge. Using data over the Continental U.S. we use a 'Perfect Model' approach in which data from a large-scale dynamical model is used as a proxy for both observations and model output.
This experimental design allows for an assessment of expected performance of SD methods in a future high-emissions climate-change scenario. We find performance is tied much more to configuration options rather than choice of SD method. In particular, proper handling of dry days (i.e., those with zero precipitation) is crucial to success.
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Although SD skill in reproducing day-to-day variability is modest (similar to 15-25%), about half that found for temperature in our earlier work, skill is much greater with regards to reproducing the statistical distribution of precipitation (similar to 50-60%). This disparity is the result of the stochastic nature of precipitation as pointed out by other authors. Distributional skill in the tails is lower overall (similar to 30-35%), although in some regions and seasons it is small to non-existent.
Even when SD skill in the tails is reasonably good, in some instances, particularly in the southeastern United States during summer, absolute daily errors at some gridpoints can be large (similar to 20 mm or more), highlighting the challenges in projecting future extremes.
Links
Get the data
- Publisher page n2t.org/ark:/85065/d78d00n8 ↗
documentation · download · from data ucar edu
Where it is published
- data.ucar.edu /dataset/evaluation-of-some-distributional-downscaling-methods-… ↗
National Center for Atmospheric Research dataset page
landing page · from data ucar edu
Catalogue records · 1
- CKAN API data.ucar.edu/api/3/action/package_show?id=3d84b63d-e55f-4957-bd2e-17e6df734… ↗
metadata API · from data ucar edu
Topics
- From keywords
- Earth & Environmental Science · Ocean & Atmospheric Science
- Inferred from text
- Climate change science 73%
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| National Center for Atmospheric Research | 3d84b63d-e55f-4957-bd2e-17e6df734098 | 13 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3702 | enrichment · data ucar edu | taxonomy-embedding@1.1.0 | title+keywords+description (73%) |
| concepts[field].local:field:earth-environmental | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| created_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| description | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /notes |
| publication_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| title | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /title |
| updated_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 |