Cai et al. (2026) Characterizing Temperature and Precipitation Tails via Expected Shortfall Regression

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

Identification

• Journal: Journal of Climate
• Year: 2026
• Date: 2026-04-10
• Authors: Peiyao Cai, X.B. Chen, Z. Zhou, Huixia Wang, Bo Li, Wen‐Xin Zhou, Ryan L. Sriver, Kean Ming Tan
• DOI: 10.1175/jcli-d-25-0024.1

Research Groups

Not specified in the abstract. The data source is NOAA’s Twentieth Century Reanalysis Project.

Short Summary

This study applies Expected Shortfall (ES) regression to analyze temperature and precipitation trends in the continental U.S. from 1950-2015, revealing distinct spatial and temporal changes in the tails of distributions, including significant decadal temperature increases in the southern and central U.S. and ENSO's influence on extreme events.

Objective

• To analyze trends in temperature and precipitation relative to time and the Niño-Southern Oscillation (ENSO) across the continental U.S. using the Expected Shortfall (ES) regression method.
• To compare the insights provided by ES regression, particularly regarding the tails of distributions, with those from mean and quantile regression.

Study Configuration

• Spatial Scale: Continental United States (U.S.).
• Temporal Scale: 1950 to 2015 (66 years), examining interannual to decadal time scales.

Methodology and Data

• Models used: Expected Shortfall (ES) regression, compared with mean regression and quantile regression.
• Data sources: Temperature and precipitation data from NOAA’s Twentieth Century Reanalysis Project.

Main Results

• ES regression results show notable differences compared to mean and quantile regression, highlighting distinct spatial and temporal trends on interannual to decadal time scales.
• Significant temperature increases were captured over the decadal time scale, particularly in the upper tails of the distribution, across the southern and central U.S.
• These findings support the disappearance of the "global warming hole" over recent decades.
• ENSO events, specifically El Niño, were found to intensify cooling effects in the Gulf Coast region during winter, particularly for the upper tails of the distribution.
• The study demonstrates the effectiveness of expected shortfall regression in estimating temperature and precipitation distributions and characterizing changes in their tails.

Contributions

• Introduces and applies Expected Shortfall (ES) regression as a novel statistical method for analyzing climate trends in temperature and precipitation distributions.
• Provides a more comprehensive description of changes in the tails of climate distributions compared to traditional mean or quantile regression.
• Identifies distinct spatial and temporal trends, particularly in extreme temperature events (upper tails), that are not fully captured by existing methods.
• Offers new evidence supporting the disappearance of the "global warming hole" using a robust statistical approach.
• Enhances the understanding of how ENSO events influence extreme temperature and precipitation patterns.

Funding

Not specified in the abstract.

Citation

@article{Cai2026Characterizing,
  author = {Cai, Peiyao and Chen, X.B. and Zhou, Z. and Wang, Huixia and Li, Bo and Zhou, Wen‐Xin and Sriver, Ryan L. and Tan, Kean Ming},
  title = {Characterizing Temperature and Precipitation Tails via Expected Shortfall Regression},
  journal = {Journal of Climate},
  year = {2026},
  doi = {10.1175/jcli-d-25-0024.1},
  url = {https://doi.org/10.1175/jcli-d-25-0024.1}
}