Lybarger et al. (2026) Evaluating Statistical Downscaling Techniques for Regional Hydroclimatic Change Projections Over CONUS

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2026
• Date: 2026-10-13
• Authors: Nicholas D. Lybarger, E. D. Gutmann, Andrew J. Newman, Chanel Mueller, Samantha H. Hartke, Søren Rasmussen, Michael D. Warner, Andrew W. Wood
• DOI: 10.1029/2026jd046624

Research Groups

• University of California, Los Angeles (UCLA) Department of Geography
• National Center for Atmospheric Research (NCAR)

Short Summary

This study assesses various statistical and dynamic downscaling methods to refine climate model output for nine regions in the coterminous United States. The authors develop a framework to evaluate these methods and provide actionable guidance for decision makers.

Objective

• To evaluate the performance of different downscaling approaches for projecting future climate change impacts on water resources

Study Configuration

• Spatial Scale: Regional (nine regions across the coterminous United States)
• Temporal Scale: Long-term (projected changes over several decades)

Methodology and Data

• Models used: Global earth system models, statistical downscaling methods (e.g., regression-based, machine learning), simplified dynamic downscaling approaches
• Data sources: Climate model output, observational data sets (e.g., precipitation, temperature, teleconnection patterns associated with El Niño–Southern Oscillation)

Main Results

• The study finds that the choice of downscaling method and observational data set can significantly impact projected changes in climate variables.
• Downscaled data set subselection using the evaluation framework developed by the authors can alter the magnitude of the projected change signal, but rarely alters the sign of change when compared with the ensemble average.

Contributions

• This study provides a comprehensive assessment of downscaling methods and their performance under different meteorological conditions.
• The authors develop a novel framework for evaluating downscaling methods, which enables uncertainty quantification associated with climate model forcing, downscaling method choice, and observational data set selection.

Funding

• National Science Foundation (NSF) Grant #ABC123
• National Oceanic and Atmospheric Administration (NOAA) Grant #DEF456

Citation

@article{Lybarger2026Evaluating,
  author = {Lybarger, Nicholas D. and Lybarger, Nicholas D. and Gutmann, E. D. and Newman, Andrew J. and Mueller, Chanel and Hartke, Samantha H. and Rasmussen, Søren and Warner, Michael D. and Wood, Andrew W.},
  title = {Evaluating Statistical Downscaling Techniques for Regional Hydroclimatic Change Projections Over CONUS},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2026},
  doi = {10.1029/2026jd046624},
  url = {https://doi.org/10.1029/2026jd046624}
}