Kalai et al. (2026) The Role of Daily and Monthly Bias Corrected Data in Preserving the Monthly Cross‐Correlation Between Precipitation and Temperature

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

Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-04-06
• Authors: Chingka Kalai, Shiqi Fang, Rajarshi Das Bhowmik, A. Sankarasubramanian
• DOI: 10.1002/joc.70351

Research Groups

Not available from the provided abstract.

Short Summary

This study evaluates the ability of two bias correction procedures, Canonical Correlation Analysis (CCA) and Quantile Regression (QR), to preserve the monthly cross-correlation between precipitation and maximum temperature from Global Climate Models (GCMs) over the Continental United States (CONUS). It finds that CCA outperforms QR in reproducing observed cross-correlations, and that bias correction applied at a daily temporal scale better preserves monthly cross-correlations compared to monthly bias correction.

Objective

• To assess the effectiveness of Canonical Correlation Analysis (CCA) and Quantile Regression (QR) bias correction procedures in preserving the monthly cross-correlation between precipitation and maximum temperature from GCMs.

Study Configuration

• Spatial Scale: Continental United States (CONUS)
• Temporal Scale: Monthly and daily for bias-corrected data; monthly for cross-correlation analysis.

Methodology and Data

• Models used: Canonical Correlation Analysis (CCA), Quantile Regression (QR)
• Data sources: Climatic variables from Global Climate Models (GCMs), observed cross-correlation data.

Main Results

• Canonical Correlation Analysis (CCA) reproduces the observed cross-correlation between precipitation and temperature better than Quantile Regression (QR).
• The removal of dry bias significantly improves the performance of all bias correction methods.
• Bias-corrected data at a daily temporal scale preserves the monthly cross-correlation more effectively compared to bias-corrected data available at a monthly temporal scale.
• For climate application studies requiring monthly forcings, it is recommended to aggregate bias-corrected daily data to a monthly temporal scale.

Contributions

• Provides a comparative analysis of CCA and QR in preserving inter-variable cross-correlations, a crucial aspect often overlooked in bias correction studies.
• Highlights the superior performance of daily bias correction over monthly bias correction for maintaining monthly cross-correlation structures.
• Emphasizes the importance of addressing dry bias for improved bias correction performance.
• Offers a practical recommendation for climate application studies regarding the temporal scale of bias correction for monthly forcings.

Funding

Not available from the provided abstract.

Citation

@article{Kalai2026Role,
  author = {Kalai, Chingka and Fang, Shiqi and Bhowmik, Rajarshi Das and Sankarasubramanian, A.},
  title = {The Role of Daily and Monthly Bias Corrected Data in Preserving the Monthly Cross‐Correlation Between Precipitation and Temperature},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70351},
  url = {https://doi.org/10.1002/joc.70351}
}