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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Thomas NOEL, Harilaos Loukos , Dimitri Defrance 


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.




Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology


climate change, CMIP6, downscaling, projections, downscaling, climate change, adaptation, impact modelling., High-resolution, adaptation, impact modelling, ERA5-Land, impact modelling, projections, CMIP6, ERA5-Land


Published: 2021-08-30 13:13

Last Updated: 2021-09-16 15:25

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CC BY Attribution 4.0 International

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