High-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with ERA5 reanalyses for climate change impact assessments

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Thomas NOEL, Harilaos Loukos , Dimitri Defrance , Mathieu Vrac , Guillaume Levavasseur


A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalyses from the Copernicus Climate Change service. The dataset is global has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). 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 meta data according to the climate modelling community standards and value checking for outlier detection.




Engineering, Life Sciences, Physical Sciences and Mathematics


climate change, CMIP5, , downscaling, ERA5, projections, High-resolutionprojections, CMIP5, ERA5, downscaling, climate change, adaptation, impact modelling., High-resolution, adaptation, impact modelling


Published: 2020-11-27 08:24

Last Updated: 2021-01-24 22:30

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CC BY Attribution 4.0 International

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Conflict of interest statement:

Data Availability (Reason not available):
Data is avalaible on demand with a commercial licence

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