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Climate change is expected to increase the frequency and intensity of rainfall extremes. Understanding future changes in rainfall is necessary for adaptation planning. Eastern Africa is vulnerable to rainfall extremes because of low adaptive capacity and high future population growth. Convection-permitting climate models have been found to better represent moderate (yearly) rainfall extremes than parameterized convection models, but there is limited analysis of rare extremes that occur less frequently than once per year. These events often have the largest socioeconomic impacts. We use extreme value theory and regional frequency analysis to quantify rare rainfall extremes over East Africa in a convection-permitting climate model (CP4A). We compare the results with its parameterized counterpart (P25), the Coordinated Regional Climate Downscaling Experiment for the African region (CORDEX-Africa) ensemble, and observations to understand how the convection parameterization impacts the results. We find that CP4A better matches observations than the parameterized models. With climate change, we find the parameterized convection models have unrealistically high changes in the shape parameter of the extreme value distribution, which controls the tail behavior (i.e., the most extreme events), leading to large increases in return levels of events with a return period of >20 years. This suggests that parameterized convection models may not be suitable for looking at relative changes in rare rainfall events with climate change and that convection-permitting models should be preferred for this type of work. With the more realistic CP4A, RCP8.5 end-of-century climate change leads to 1-in-100-yr events becoming 1-in-23-yr events, which will necessitate serious adaptation efforts to avoid devastating socioeconomic impacts. Significance Statement: We use a new, high-resolution climate model to examine how rare extreme rainfall events in East Africa might change in the future with climate change and compare the results with those from standard-resolution climate models. We find that the standard-resolution models have unrealistically large increases in rainfall for events that occur less frequently than every 20 years. The high-resolution model is more realistic and is required to illustrate possible future changes in rare rainfall extremes. Extreme events will become more common with climate change, and in the more realistic model we show that a 1-in-100-yr event may become a 1-in-23-yr event by the end of the century if greenhouse gas emissions are not significantly reduced. [ABSTRACT FROM AUTHOR]

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics, and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The Global Challenges Research Fund (GCRF) African Science for Weather Information and Forecasting Techniques (SWIFT) project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement, and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps. Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training, modeling and operational prediction, and communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather forecasting "Testbeds"—the first of their kind in Africa—which have been used to bring new evaluation tools, research insights, user perspectives, and communications pathways into a semioperational forecasting environment. [ABSTRACT FROM AUTHOR]

Forecasting convective rainfall in the tropics is a major challenge for numerical weather prediction. The use of convection-permitting (CP) forecast models in the tropics has lagged behind the midlatitudes, despite the great potential of such models in this region. In the scientific literature, there is very little evaluation of CP models in the tropics, especially over an extended time period. This paper evaluates the prediction of convective storms for a period of 2 years in the Met Office operational CP model over East Africa and the global operational forecast model. A novel localized form of the fractions skill score is introduced, which shows variation in model skill across the spatial domain. Overall, the CP model and the global model both outperform a 24-h persistence forecast. The CP model shows greater skill than the global model, in particular on subdaily time scales and for storms over land. Forecasts over Lake Victoria are also improved in the CP model, with an increase in hit rate of up to 20%. Contrary to studies in the midlatitudes, the skill of both models shows a large dependence on the time of day and comparatively little dependence on the forecast lead time within a 48-h forecast. Although these results provide more motivation for forecasters to use the CP model to produce subdaily forecasts with increased detail, there is a clear need for more in situ observations for data assimilation into the models and for verification. A move toward ensemble forecasting could have further benefits. [ABSTRACT FROM AUTHOR]

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Talib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)