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. The objective of this paper is to present a new dataset of bias-correctedCMIP5 global climate model (GCM) daily data over Africa. This dataset wasobtained using the cumulative distribution function transform (CDF-t) method,a method that has been applied to several regions and contexts but never toAfrica. Here CDF-t has been applied over the period 1950–2099 combiningHistorical runs and climate change scenarios for six variables: precipitation,mean near-surface air temperature, near-surface maximum air temperature,near-surface minimum air temperature, surface downwelling shortwaveradiation, and wind speed, which are critical variables for agriculturalpurposes. WFDEI has been used as the reference dataset to correct the GCMs.Evaluation of the results over West Africa has been carried out on a list ofpriority user-based metrics that were discussed and selected withstakeholders. It includes simulated yield using a crop model simulating maizegrowth. These bias-corrected GCM data have been compared with anotheravailable dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset.The impact of WFD, WFDEI, and also EWEMBI reference datasets has been alsoexamined in detail. It is shown that CDF-t is very effective at removing thebiases and reducing the high inter-GCM scattering. Differences with otherbias-corrected GCM data are mainly due to the differences among thereference datasets. This is particularly true for surface downwellingshortwave radiation, which has a significant impact in terms of simulatedmaize yields. Projections of future yields over West Africa are quitedifferent, depending on the bias-correction method used. However all theseprojections show a similar relative decreasing trend over the 21st century.

A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies