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<p>A high-resolution climate projections dataset is produced by statistically downscaling climate projections from the CMIP6 experiment. This global dataset is at a spatial resolution of 0.0375° × 0.0375° from 19 climate models over Senegal domain. It includes five essential surface daily variables: mean, minimum, and maximum air temperatures, precipitation, and terrestrial radiation. The dataset covers daily climate data for the historical period (1850–2014) and future projections (2015–2100) for three greenhouse gas emissions scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The downscaling method used is the “Cumulative Distribution Function-transform”, which is utilized for bias correction and has been widely referenced in peer-reviewed literature. The data processing includes rigorous quality control of metadata following climate modelling community standards and outlier detection to ensure data integrity.</p>

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High-Resolution Downscaled CMIP6 Projections dataset of Key Climate Variables for Senegal

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月20日

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  • Asse Mbengue,
  • Benjamin Sultan,
  • Redouane Lguensat,
  • Mathieu Vrac,
  • Aïda Diongue-Niang,
  • Ousmane Ndiaye,
  • Amadou Thierno Gaye

摘要

A high-resolution climate projections dataset is produced by statistically downscaling climate projections from the CMIP6 experiment. This global dataset is at a spatial resolution of 0.0375° × 0.0375° from 19 climate models over Senegal domain. It includes five essential surface daily variables: mean, minimum, and maximum air temperatures, precipitation, and terrestrial radiation. The dataset covers daily climate data for the historical period (1850–2014) and future projections (2015–2100) for three greenhouse gas emissions scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The downscaling method used is the “Cumulative Distribution Function-transform”, which is utilized for bias correction and has been widely referenced in peer-reviewed literature. The data processing includes rigorous quality control of metadata following climate modelling community standards and outlier detection to ensure data integrity.