Evaluation of CMIP6 Models in Simulating the Statistics of Extreme Precipitation Over Eastern Africa
We assess the representation of extreme precipitation over Eastern Africa in newly developed Earth system models (i.e. CMIP6). The CMIP6 models used herein tend to overestimate the total wet-day precipitation and consecutive wet days and underestimate very wet days and maximum 5-day precipitation in both seasons (March-May and September–November). Overall, the CMIP6 model performance over Eastern Africa varies with the season and index under consideration and is generally independent of horizontal resolution.
Extreme precipitation events can have large impacts on society and Earth system models can be useful tools for understanding the variability and changes in these events. Our study highlights the seasons where CMIP6 models perform well and where they do not, which is critical for assessing confidence in their simulations.
The Eastern Africa region experiences frequent extreme precipitation events that can cause the destruction of property and the environment and the loss of lives. Thus, there is a need to understand how these events may change in the future and how well the global climate models that are used to make projections can simulate precipitation extremes in this region before they can be used in downscaling or flood and drought impact assessment studies. In this work, we evaluated the ability of sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models to simulate present-day precipitation extremes over the Eastern Africa region during the two rainy seasons (March-May and September–November). We used nine extreme precipitation indices (including seven (one) indices of wet (dry) extremes) defined by the Expert Team on Climate Change Detection and Indices. The CMIP6 models were evaluated against two gridded observation datasets: Global Precipitation Climatology Project One-Degree Daily Dataset and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42. Three model performance metrics (percentage bias, normalized root-mean-square error, and pattern correlation coefficient) were employed to further assess the strengths and weaknesses of the models. Our results show that the multi-model ensemble mean generally provides a better representation of observed precipitation and related extremes compared to individual models when considering all metrics and seasons. Several consistent biases are evident across CMIP6 models, which tend to overestimate the total wet- day precipitation and consecutive wet days and underestimate very wet days and maximum 5-day precipitation in both seasons. Furthermore, no single model consistently performs best, model performance varies with the season and index under consideration and is generally independent of horizontal resolution.