This study evaluates the ability of the Coordinated Output for Regional Evaluations (CORE) initiated under the Coordinated Regional Climate Downscaling Experiment (CORDEX) to reproduce austral summer extreme precipitation indices over the Zambezi River basin (ZRB) from 1983 to 2005. The ability of the RCMs to simulate the spatial distributions of observed extreme precipitation was assessed using multiple datasets. The spatially averaged performance of the CORDEX‐CORE ensemble mean (CORE‐ENS) and RCM ensembles (RCM‐ENSEMBLES developed from different combinations of CORE‐ENS ensemble members) was evaluated using statistical metrics. The results showed similarities in the spatial distribution of extreme precipitation among the observations. However, there were considerable differences in the magnitude of extreme precipitation among the observations. The frequency of days with very heavy precipitation (R20) showed the largest differences in magnitude among the observations. Although there are differences in magnitude, CORE‐ENS, its ensemble members, and RCM‐ENSEMBLES can capture the spatial distribution of extreme precipitation. Specifically, CORE‐ENS, its ensemble members, and RCM‐ENSEMBLES overestimated (underestimated) the frequency of rainy days (RR1) and maximum consecutive wet days (CWD) (maximum consecutive dry days [CDD]). In contrast, some members of CORE‐ENS overestimate, while others underestimate, the simple daily intensity index (SDII), the frequency of days with heavy precipitation (R10), and R20, depending on the choice of reference data. The Consortium for Small‐scale MOdelling in CLimate Mode (CCLM) and Regional MOdel (REMO) performed better than the Regional Climate Model (RegCM4.7) in simulating the spatial distributions of extreme precipitation. The regionally averaged uncertainties (biases) of the CORE‐ENS, its ensemble members, and RCMENSEMBLES were within the range of those in the observational datasets, except for CWD (CWD and RR1). Overall, CORE‐ENS performed better than its ensemble members in simulating extreme precipitation.
This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.
This study examines the potential implications of 1.5, 2.0, and 3.0°C global warming levels (GWLs) on the austral summer (November–March) extreme precipitation indices over the Zambezi River basin (ZRB) relative to the control period (1971–2000). We computed extreme precipitation based on daily data from observations and the Coordinated Regional Downscaling Experiment (CORDEX)‐Coordinated Output for Regional Evaluations (CORE) multi‐model ensemble mean (ENSMean). First, we evaluated the performance of the CORDEX‐CORE ENSMean in simulating extreme precipitation based on six indices; the number of rainy days (RR1), simple daily intensity index (SDII), maximum consecutive wet days (CWD), maximum consecutive dry days (CDD), heavy precipitation days (R10), and very heavy precipitation days (R20). The results indicate that the CORDEX‐CORE ENSMean can simulate the spatial distributions of extreme precipitation over the ZRB. However, CORDEX‐CORE largely overestimates the magnitudes of RR1 and CWD. The projected changes show a decrease in RR1, CWD, and R10 under all GWLs, with a robust and pronounced decrease under 3.0°C GWL. In contrast, CDD and SDII are projected to increase under all GWLs, with a robust and pronounced increase in CDD under 3.0°C GWL. The regionally averaged changes show that the median values of CWD, RR1, and R10 (SDII and CDD) are projected to decrease (increase) under all GWLs over the ZRB and sub‐basins. The probability density function (PDF) shows negative (positive) shifts in the mean of CWD, RR1, and R10 (SDII and CDD) over the ZRB and sub‐basins under all GWLs. In contrast, R20 is projected to increase (decrease) over most of the western (eastern) ZRB under all GWLs. Assessing the implications of an additional 0.5 and 1.5°C (1.0°C) warming to 1.5°C (2.0°C) GWL shows that limiting GWL to 1.5°C would restrict the future exposure of the ZRB to extreme precipitation.
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