Weber et al. (2026) SEAS5-BCSD: a bias-corrected and downscaled global seasonal forecast reference dataset for 1981–2024

Identification

• Journal: Earth system science data
• Year: 2026
• Date: 2026-07-22
• Authors: Jan Niklas Weber, Christof Lorenz, Tanja C. Schober, Rebecca Wiegels, Harald Günter Kunstmann
• DOI: 10.5194/essd-18-5259-2026

Research Groups

• Karlsruhe Institute of Technology, Campus Alpin, Institute of Meteorology and Climate Research IMKIFU, 82467 Garmisch-Partenkirchen, Germany
• Institute of Geography, University of Augsburg, 86159 Augsburg, Germany
• Center for Climate Resilience, University of Augsburg, 86159 Augsburg, Germany

Short Summary

This study presents a bias-corrected and downscaled global seasonal forecast reference dataset (SEAS5-BCSD) for precipitation and 2 m temperature from 1981 to 2024. The dataset is generated using the Bias Correction and Spatial Disaggregation (BCSD) method, combining ECMWF SEAS5 seasonal forecasts with ERA5 reanalysis data.

Objective

• To develop a global bias-corrected seasonal forecast reference dataset for precipitation and 2 m temperature from 1981 to 2024.
• To evaluate the performance of the SEAS5-BCSD dataset using various metrics, including the Continuous Ranked Probability Skill Score (CRPSS) and Brier Skill Score (BSS).

Study Configuration

• Spatial Scale: Global land areas at a spatial resolution of 0.25° x 0.25°.
• Temporal Scale: Monthly resolution from 1981 to 2024.

Methodology and Data

• Models used: ECMWF SEAS5 seasonal forecasting system and ERA5 reanalysis data.
• Data sources: ECMWF SEAS5 seasonal forecasts, ERA5 reanalysis data, and historical reforecasts for 1981–2016.

Main Results

• The SEAS5-BCSD dataset outperforms the climatology by a large margin across nearly all land regions, with particularly strong gains observed for temperature.
• The CRPSS values exceed 0.15 globally for precipitation and SPEI-1, and 0.25 for temperature in many regions.
• The Brier Skill Score (BSS) performance is also evaluated, showing that the SEAS5-BCSD dataset outperforms the climatology.

Contributions

• This study provides a comprehensive global assessment of bias-corrected seasonal forecasts using the SEAS5-BCSD dataset.
• The dataset can be used for various applications, including drought prediction studies and water resource management.

Funding

• This research was funded by [list projects, programs, and reference codes that funded this research].

Citation

@article{Weber2026SEAS5BCSD,
  author = {Weber, Jan Niklas and Lorenz, Christof and Schober, Tanja C. and Wiegels, Rebecca and Kunstmann, Harald Günter},
  title = {SEAS5-BCSD: a bias-corrected and downscaled global seasonal forecast reference dataset for 1981–2024},
  journal = {Earth system science data},
  year = {2026},
  doi = {10.5194/essd-18-5259-2026},
  url = {https://doi.org/10.5194/essd-18-5259-2026}
}