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.18334

Research Groups

• University of Hamburg (AG HAREME, Department of Physical Geography)
• High Resolution Model Intercomparison Project (HighResMIP) modeling groups (MPI-ESM1-2-XR, EC-Earth3P-HR, CNRM-CM6-1-HR, HadGEM3-GC31-HM)
• Earth System Grid Federation (ESGF)

Short Summary

The BC-HiRMIP dataset offers globally consistent, bias-corrected climate data derived from four HighResMIP global climate models at daily temporal and 0.5° spatial resolution, covering historical (1979-2014) and future (2015-2050, SSP5-8.5) periods, and includes 11 essential meteorological variables for impact assessment studies.

Objective

• To generate a comprehensive, globally consistent, and bias-corrected climate dataset from HighResMIP experiments suitable for impact assessment studies.

Study Configuration

• Spatial Scale: Global coverage at 0.5° x 0.5° horizontal resolution (approximately 55 km x 55 km at the equator).
• Temporal Scale: Daily resolution, covering the period from 1979-01-01 to 2050-12-31 (historical: 1979-2014; future: 2015-2050).

Methodology and Data

• Models used: Four Global Climate Models (GCMs) from the HighResMIP project: MPI-ESM1-2-XR, EC-Earth3P-HR, CNRM-CM6-1-HR, HadGEM3-GC31-HM.
• Data sources:
  • Raw climate model outputs from HighResMIP experiments (historical and highres-future under the SSP5-8.5 scenario pathway).
  • Observational reference dataset: W5E5 V2.0 (Lange et al., 2021) at 0.5° resolution.
  • Bias correction method: ISIMIP3BASD v2.5 (parametric quantile mapping approach).
  • Data remapping: CDO (conservative remapping for flux variables like precipitation and radiation; bilinear remapping for other continuous variables).

Main Results

• The BC-HiRMIP dataset provides bias-corrected daily climate data for 11 essential meteorological variables, including near-surface air temperature (mean, minimum, maximum, in Kelvin), precipitation (sum, in kilograms per square meter per second), surface downwelling radiation (shortwave and longwave, in Watts per square meter), near-surface wind speed (in meters per second), near-surface relative humidity (in percent), near-surface specific humidity (in kilograms per kilogram), and surface air pressure (in Pascals).
• The dataset spans a global extent from 1979 to 2050, derived from four high-resolution GCMs with equilibrium climate sensitivities ranging from 2.99 °C to 5.62 °C.
• The applied bias correction method effectively adjusts systematic biases while preserving climate change signals and maintaining physical relationships between variables, with its performance validated across diverse Köppen-Geiger climate types.

Contributions

• Delivers a comprehensive, globally consistent, and bias-corrected high-resolution climate dataset specifically designed for climate impact assessment studies, thereby enhancing the reliability of future climate projections.
• Improves the usability of HighResMIP model outputs by correcting systematic biases and ensuring physical consistency, making them more robust for a wide range of downstream applications.
• Offers a broad suite of meteorological variables, increasing the versatility and applicability of the dataset 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.18334},
  url = {https://doi.org/10.25592/uhhfdm.18334}
}