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            <title xml:lang="en">A diagnostic evaluation of precipitation in CORDEX models over Southern Africa.</title>
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            <funder>Research supported by the Global Change System for Analysis, Research, and Training (START) in partnership with the University of Cape Town's Climate Systems Analysis Group, the World Climate Research Program, the Climate and Development Knowledge Network, the International Centre for Theoretical Physics, the Swedish Meteorological and Hydrological Institute, and the European Union Seventh Framework Programme (FP7; 2012-2013) under Grant Agreement 316167.</funder>
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              <p>The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of ;50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim; 1989-2008). Results are compared against a number of observational datasets. In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain. Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Nño (La Niña) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.</p>
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