Akaffou et al. (2026) Comparing bias adjustment methods for CMIP6 extreme precipitation projections in the San-Pédro River Basin (Côte d’Ivoire, West Africa)

Identification

• Journal: Theoretical and Applied Climatology
• Year: 2026
• Date: 2026-02-21
• Authors: Franck Hervé Akaffou, Salomon Obahoundje, Bérenger Koffi, Gnibga Issoufou Yangouliba, Wawogninlin Brice Coulibaly, Konan Jean-Yves N’guessan, Arona Diedhiou, Kouakou Lazare KOUASSI
• DOI: 10.1007/s00704-026-06043-y

Research Groups

• Département des Sciences de la Terre, Université Jean Lorougnon Guédé, Daloa, Côte d’Ivoire
• International Water Management Institute (IWMI), Accra, Ghana
• Institut National Polytechnique Félix Houphouët Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire
• Sciences Transversales/Géomatique, Université Virtuelle du Burkina Faso, Ouagadougou, Burkina Faso
• Competence Center, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Ouagadougou, Burkina Faso
• Laboratoire des Sciences de la Matière, de l’Environnement et de l’Energie Solaire (LASMES) – African Centre of Excellence on Climate Change, Biodiversity and Sustainable Development, Université Félix Houphouët Boigny, Abidjan, Côte d’Ivoire
• Université Grenoble Alpes, Grenoble, France

Short Summary

This study evaluates four bias adjustment methods for CMIP6 extreme precipitation projections in the San-Pédro River Basin, Côte d’Ivoire, identifying CDFt SSR as the most robust for accurately reproducing observed distributions and projecting increased extreme precipitation, which heightens flood risks.

Objective

• To analyze the evolution of extreme precipitation during the baseline period (1991–2020).
• To evaluate the effectiveness of bias adjustment methods in adjusting extreme precipitation values.
• To assess the suitability of bias adjustment methods for the calculation of precipitation indices.
• To project future precipitation extremes based on precipitation indices in the near (2031–2060) and far (2061–2090) futures over the San-Pédro River basin.

Study Configuration

• Spatial Scale: San-Pédro River basin, Côte d’Ivoire, West Africa, with an area of 2.432 x 10^9 m². The Fayé hydropower dam lake covers 1.629 x 10^7 m² with 1.124 x 10^7 m² of flood zones. Analyses were performed at a spatial resolution of 0.05° x 0.05°.
• Temporal Scale:
  • Baseline/Climatic Normal: 1991–2020
  • Historical (CMIP6): 1982–2014
  • Bias adjustment calibration: 1982–2001 (20 years)
  • Bias adjustment validation: 2002–2014 (13 years)
  • Near Future projections: 2031–2060
  • Far Future projections: 2061–2090
  • Analyses conducted at annual and long rainy season (April to July) timescales.

Methodology and Data

• Models used:
  • Seventeen Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and their multi-model ensemble mean (EnsMean).
  • Four bias adjustment methods: Cumulative Distribution Function Transfert Singularity Stochastic Removal (CDFt SSR), Empirical Quantile Mapping (Eqm), Delta Change (Delta), and Linear Scaling (Scaling).
  • Seven flood-related precipitation indices from the Expert Team on Climate Change Detection and Indices (ETCCDI): PRCPTOT (total precipitation), R1mm (number of wet days), SDII (simple daily intensity index), CWD (consecutive wet days), R99pTOT (precipitation from very wet days), Rx5day (maximum 5-day precipitation), and Rx1day (maximum 1-day precipitation).
  • Statistical evaluation metrics: Correlation coefficient (R) and percentage of bias (Pbias).
  • Trend analysis: Mann-Kendall test.
• Data sources:
  • Observational data: Climate Hazard Group Infrared Precipitation with Station (CHIRPS) satellite-based precipitation product at 0.05° resolution (1982–2020).
  • CMIP6 GCM outputs for precipitation (daily timescale) under Shared Socioeconomic Pathways (SSPs) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5.

Main Results

• Baseline Period (1991–2020): Most indices showed consistent trends across annual and long rainy season scales, indicating a shift towards more frequent but less intense precipitation events, accompanied by a decline in extreme precipitation. SDII was generally higher during the long rainy season.
• Bias Adjustment Performance:
  • Delta-adjusted models achieved the best statistical performance (R > 0.8 and Pbias < 30%).
  • CDFt SSR-adjusted models most accurately reproduced observed daily precipitation and indices distributions, particularly for PRCPTOT, R1mm, SDII, Rx5day, and Rx1day. Eqm showed similar performance.
  • Delta and Scaling methods struggled to adjust outliers and significantly overestimated R1mm and CWD, while underestimating SDII and R99pTOT.
  • Limitations in accurately reproducing CWD and R99pTOT persisted even with CDFt SSR and Eqm.
• Future Projections (2031–2090, relative to 1985–2014, using CDFt SSR EnsMean):
  • Consistent increases in SDII (up to 3.2%), Rx5day (up to 16.3%), and Rx1day (up to 24%) across all scenarios and projection periods.
  • Slight decreases in CWD (up to 1 day).
  • Minor changes for PRCPTOT (increases up to 1.7%) and R1mm (projected to remain stable or show slight decrease).
  • R99pTOT is projected to decrease by a maximum of 4.2%.
  • The highest increases for Rx5day and Rx1day are projected under SSP5-8.5, with greater magnitude and variability in the far future.
  • Overall, precipitation intensity and extreme precipitation events (Rx1day, Rx5day) are projected to increase, while total precipitation and the number of rainy days remain relatively stable.
  • Projected warming of 1 K, 2 K, and 3.5 K in the near, mid, and far future, respectively, contributes to the intensification of extreme precipitation.

Contributions

• This study is the first in Côte d’Ivoire to comprehensively evaluate four bias adjustment methods for refining CMIP6 extreme precipitation projections, specifically for ETCCDI indices, at daily and seasonal timescales in the San-Pédro River Basin.
• It identifies CDFt SSR as the most robust bias adjustment method for projecting extreme precipitation in the region, providing a crucial foundation for climate impact assessments and adaptation planning in West Africa.
• The research offers critical insights into future trends of flood-related precipitation extremes in a vulnerable, understudied rural basin with significant hydropower infrastructure, highlighting potential threats to local communities, agriculture, and dam management.

Funding

• Programme d’Appui Stratégique à la Recherche Scientifique (PASRES) [CSRS/PASRES N°262].
• Laboratoire Mixte International (LMI NEXUS) and Laboratoire des Sciences et Technologies de l’Environnement.
• Centre National de Calcul de Côte d’Ivoire (CNCCI) for providing the Coupled Model Intercomparison Project Phase 6 facility.

Citation

@article{Akaffou2026Comparing,
  author = {Akaffou, Franck Hervé and Obahoundje, Salomon and Koffi, Bérenger and Yangouliba, Gnibga Issoufou and Coulibaly, Wawogninlin Brice and N’guessan, Konan Jean-Yves and Diedhiou, Arona and KOUASSI, Kouakou Lazare},
  title = {Comparing bias adjustment methods for CMIP6 extreme precipitation projections in the San-Pédro River Basin (Côte d’Ivoire, West Africa)},
  journal = {Theoretical and Applied Climatology},
  year = {2026},
  doi = {10.1007/s00704-026-06043-y},
  url = {https://doi.org/10.1007/s00704-026-06043-y}
}