Yakubu et al. (2026) A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-02-23
• Authors: Fuseini Jacob Yakubu, Jürgen Böhner, Laurens M. Bouwer, Shabeh ul Hasson
• DOI: 10.25592/uhhfdm.18335

Research Groups

• University of Hamburg (HAREME group, Physical Geography Department)
• Zentrum für Nachhaltiges Forschungsdatenmanagement (ZFDM)

Short Summary

This paper introduces the BC-HiRMIP dataset, a globally consistent, comprehensive collection of bias-corrected climate data from High Resolution Model Intercomparison Project (HighResMIP) experiments, designed for impact assessment studies. It provides daily meteorological variables at 0.5° longitude by 0.25° latitude spatial resolution for historical (1979-2014) and future (2015-2050) periods.

Objective

• To provide a globally consistent and comprehensive bias-corrected climate dataset derived from HighResMIP experiments for use in impact assessment studies.

Study Configuration

• Spatial Scale: Global coverage at 0.5 degrees longitude by 0.25 degrees latitude horizontal resolution (approximately 55 km by 28 km at the equator).
• Temporal Scale: Daily resolution, covering the period from 1979 to 2050. This includes a historical period (1979-01-01 to 2014-12-31) and a future projection period (2015-01-01 to 2050-12-31) following the SSP5-8.5 scenario pathway.

Methodology and Data

• Models used:
  • MPI-ESM1-2-XR
  • EC-Earth3P-HR
  • CNRM-CM6-1-HR
  • HadGEM3-GC31-HM
  • Bias correction method: ISIMIP3BASD v2.5 (parametric quantile mapping approach)
• Data sources:
  • High Resolution Model Intercomparison Project (HighResMIP) experiments outputs.
  • W5E5 V2.0 observational reference dataset at 0.5 degree resolution for bias correction.
  • Remapping from native model grids to the reference dataset grid using CDO (conservative for flux variables, bilinear for continuous variables).

Main Results

• The creation of the BC-HiRMIP dataset, a globally consistent and comprehensive bias-corrected climate dataset.
• The dataset includes up to 11 essential meteorological variables: daily mean, minimum, and maximum near-surface air temperature (K); daily sum of precipitation (kg m⁻² s⁻¹); daily sum of precipitation in form of snow (kg m⁻² s⁻¹); daily mean surface downwelling shortwave radiation (W m⁻²); daily mean surface downwelling longwave radiation (W m⁻²); daily mean near-surface wind speed (m s⁻¹); daily mean near-surface relative humidity (percent); daily mean near-surface specific humidity (kg kg⁻¹); and daily mean surface air pressure (Pa).
• The bias correction method successfully adjusts systematic biases while preserving climate change signals, with its performance validated across diverse Köppen-Geiger climate types.
• The dataset is available in netCDF format, totaling approximately 773.2 GB.

Contributions

• Provides a ready-to-use, high-resolution, bias-corrected climate dataset specifically tailored for impact assessment studies, addressing a critical need for reliable climate projections.
• Applies a robust and validated bias-correction method (ISIMIP3BASD v2.5) that preserves climate change signals and physical relationships between variables.
• Consolidates and processes data from four prominent HighResMIP global climate models, offering a multi-model ensemble for enhanced reliability.
• Offers a comprehensive suite of meteorological variables, making it versatile for various climate impact research domains.

Funding

• Acknowledges multiple funding agencies that support HighResMIP initiatives and the Earth System Grid Federation (ESGF).

Citation

@article{Yakubu2026BiasCorrected,
  author = {Yakubu, Fuseini Jacob and Böhner, Jürgen and Bouwer, Laurens M. and Hasson, Shabeh ul},
  title = {A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies},
  journal = {Open MIND},
  year = {2026},
  doi = {10.25592/uhhfdm.18335},
  url = {https://doi.org/10.25592/uhhfdm.18335}
}