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This article evaluates the ability of the Coordinated Regional Downscaling Experiment (CORDEX) regional climate models (RCMs) in simulating monthly rainfall variation during the austral summer half year (October to March) over southern Africa, the timing of the rainy season and the relative frequencies of rainfall events of varying intensities. The phasing and amplitude of monthly rainfall evolution and the spatial progression of the wet season onset are well simulated by the models. Notwithstanding some systematic biases in a few models, the simulated onset and end of the rainy season and their interannual variability are highly correlated with those computed from the reference data. The strongest agreements between the reference and modelled precipitation patterns are found north of about 20∘S in the vicinity of the Inter Tropical Convergence Zone. A majority of the RCMs adequately capture the reference precipitation probability density functions, with a few showing a bias towards excessive light rainfall events. ; The CORDEX-Africa programme was supported by the Global Change System for Analysis, Research, and Training (START) through the Climate Systems Analysis Group of the University of Cape Town. Support from the World Climate Research Program (WCRP), the Climate and Development Knowledge Network (CDKN), the International Centre for Theoretical Physics (ICTP), the Swedish Meteorological and Hydrological Institute (SMHI) and the European Union Seventh Framework Programme. ; http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1530-261X ; hb2016 ; Geography, Geoinformatics and Meteorology

Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020). ; This research has been supported by the Spanish National Plan for Scientific and Technical Research and Innovation (project PID2019-111481RB-I00 and María de Maeztu excellence programme projects MdM-2017-0765 and MdM-2017-0714), FCT MCTES financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020), and the Basque Government BERC 2018–2021 programme. ; Peer reviewed