This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.dib.2022.108669. This is version 3 of this Preprint.
A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments
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Authors
Thomas NOEL,
Harilaos Loukos,
Dimitri Defrance 
Abstract
A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.
DOI
https://doi.org/10.31223/X5S610
Subjects
Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology
Keywords
climate change, CMIP6, downscaling, projections, downscaling, climate change, adaptation, impact modelling., High-resolution, adaptation, impact modelling, ERA5-Land, impact modelling, projections, CMIP6, ERA5-Land
Dates
Published: 2021-08-30 02:13
Last Updated: 2022-10-24 07:36
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