Changes in extreme precipitation events in the Zambezi River basins based on CORDEX‐CORE models: Part I—Evaluation of historical simulation

Abstract

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.

Exploring the Influence of Improved Horizontal Resolution on Extreme Precipitation in Southern Africa Major River Basins: Insights from CMIP6 HighResMIP Simulations

“…In an assessment of precipitation estimate products across Africa, Mekonnen et al (2023) reported that TAMSAT performed poorly in estimating daily precipitation, while MSWEP and CHIRPS demonstrated good performance. The variation among global observational data sets identified in this study align with those of previous studies (Alexander et al 2020;Dosio et al 2021a;Faye and Akinsanola, 2022;Samuel et al 2022). Given the significant discrepancies among observations highlighted in this work, users should exercise caution when using these observations to assess climate models in the southern African region.…”

“…A notable constraint in the assessment of climate model simulations in Africa is the lack of reliable gauge-based observational datasets (Samuel et al 2023a;Weber et al 2023;Tamoffo et al 2023;Dosio et al 2022). Previous studies (Alexander et al 2020;Dosio et al 2021a;Faye and Akinsanola, 2022;Samuel et al 2022) have identified significant inconsistencies among gridded datasets across Africa.…”

“…The datasets used in this study have been utilized in previous works to assess the performance of climate models in reproducing precipitation (Samuel et al 2022;Moradian et at. 2023;Gnitou et al 2023;Samuel et al 2023a).…”

“…. Moreover, simulating precipitation across southern Africa is a challenging task for climate models, primarily due to the influence of various global and regional processes on the region's climate (Reason et al 2006;Driver et al 2019;Desbiolles et al 2020;Samuel et al 2022). Previous studies (Zhang et al 2016;Iles et al 2020;Strandberg et al 2021;Jong et al 2023;Jin et al 2023) have highlighted that enhancing the spatial resolution of GCMs could be useful for improving their ability to simulate precipitation characteristics.…”

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa. Part I: Assessment of the historical simulations

“…The evaluation of historical climate simulations is fundamental, especially before they are used to assess the future impacts of extreme precipitation on key sectors such as agriculture and water resources. This is particularly important over southern Africa because of the di culties of climate models in representing precipitation over the region (Desbiolles et al 2020;Samuel et al 2022).…”