Abstract:
In this study, we analysed trends of daily precipitation-based indices in the ensembles of Global and
Regional Climate Models (GCMs and RCMs) for historical and future climate projection. The specific
objectives of the work were to investigate the variability and trend of extreme precipitation, assess
models capability for extreme precipitation studies over Africa and make projections of future extreme
precipitation. First, we evaluated the ability of the state-of-the-art GCMs and RCMs to reproduce the
mean and spatial characteristics of extreme precipitation indices over the Africa domain. In particular,
the extent to which CORDEX (COordinated Regional Downscaling Experiment) adds useful details on
the performance of CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodel ensemble was
investigated. Comparison of the present day simulation was performed with two precipitation
observation datasets, the high resolution TRMM (Tropical Rainfall Measuring Mission) and coarse
resolution GPCP (Global Precipitation Climatology Project), to evaluate models strengths and
weaknesses. Trends of changes in extreme precipitation indices in the 21st century under the most
extreme IPCC (Intergovernmental Panel on Climate Change) emission scenario (RCP8.5 -
Representative Concentration Pathway), and projected by ensembles of both CMIP5 GCMs and
CORDEX RCMs were also examined. Eight indices generated from absolute (1mm) and percentile (95th)
based thresholds as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI)
were computed for seventeen CMIP5 GCMs and six CORDEX RCMs (for twelve downscaling
experiments) for each year during the historical (1975 - 2004) and future (2006 - 2099) periods, before
long-term means and multimodel ensembles were applied. For comparison purposes, both the validation
datasets and model outputs were interpolated onto the GPCP grid (100 km) through a bilinear
interpolation. Statistical evaluation metrics including mean bias - MB, standard deviation, centered rootmean-
square error (RMSE) and correlation were performed over three sub-regions (Sahel, Northern East
Africa and Central Southern Africa) having different characteristics of the annual cycle of rainfall.
