Prolonged periods of extreme heat also known as heatwaves are a growing concern in a changing climate. Over the Sahel, a hot and semi-arid region in West Africa, they are still relatively poorly understood and managed. In this research, five multivariate thermal indices derived from the ERA5 database were used to characterize Sahelian heatwaves for statistical analysis and as a sampling basis to investigate their underlying thermodynamic causes. Results show that on average most locations in the Sahel suffer from one or two heatwaves a year lasting 3–5 days but with severe magnitude. The eastern Sahel is more at risk than the west, experiencing more frequent and longer lasting events. Despite similar statistics of intensity, duration and frequency across the heatwave indices, for a given diurnal phase, there is surprisingly low agreement in the timing of events. Furthermore daytime and nighttime heatwaves have little synchronicity. In terms of associated thermodynamic processes, heat advection and the greenhouse effect of moisture are identified as the main causes of Sahelian heatwaves. The processes are nevertheless sensitive to the indices, consequence of the distinctness of their respective samples. Therefore attention should be given to the choice of either index in operational monitoring and forecasting of heatwaves. This will allow to effectively target different exposed socio-economic groups and resultantly enhance the efficiency of early warning systems.

Heatwaves pose a serious threat to human health worldwide but remain poorly documented over Africa. This study uses mainly the ERA5 dataset to investigate their large-scale drivers over the Sahel region during boreal spring, with a focus on the role of tropical modes of variability including the Madden–Julian Oscillation (MJO) and the equatorial Rossby and Kelvin waves. Heatwaves were defined from daily minimum and maximum temperatures using a methodology that retains only intraseasonal scale events of large spatial extent. The results show that tropical modes have a large influence on the occurrence of Sahelian heatwaves, and, to a lesser extent, on their intensity. Depending on their convective phase, they can either increase or inhibit heatwave occurrence, with the MJO being the most important of the investigated drivers. A certain sensitivity to the geographic location and the diurnal cycle is observed, with nighttime heatwaves more impacted by the modes over the eastern Sahel and daytime heatwaves more affected over the western Sahel. The examination of the physical mechanisms shows that the modulation is made possible through the perturbation of regional circulation. Tropical modes thus exert a control on moisture and the subsequent longwave radiation, as well as on the advection of hot air. A detailed case study of a major event, which took place in April 2003, further supports these findings. Given the potential predictability offered by tropical modes at the intraseasonal scale, this study has key implications for heatwave risk management in the Sahel.

Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with pre-emptive action, however currently available early warnings are limited to a few days lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper quintile rainfall within a season, at lead times of two weeks and beyond. We demonstrate the link between the Madden-Julian Oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia river in Western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.

Global warming has increased the frequency of extreme weather events, including heatwaves, over recent decades. Heat early warning systems are being set up in many regions as a tool to mitigate their effects. Such systems are not yet implemented in the West African Sahel, partly because of insufficient knowledge on the skill of models to predict them. The present study addresses this gap by examining the skill of the ECMWF ENS extended-range forecasting system (ENS-ext) to predict Sahelian heatwaves out to subseasonal lead-times. It also assesses the importance of tropical modes of variability, which were previously identified as important large-scale drivers of heatwave occurrence in the Sahel. The results show that ENS-ext is able to predict Sahelian heatwaves with significant skill out to lead-week 2–3. With increasing lead-time, heatwaves are more predictable at nighttime than at daytime. Likewise, the pre-monsoon season heatwaves have a longer predictability than those occurring in late winter. The model is also able to relatively well simulate the observed relationship between heatwave occurrence and tropical mode activity. Furthermore, the prediction skill is better during the active phases of the modes, suggesting that they are good sources of heatwave predictability. Therefore, improving the representation of tropical modes in models will positively impact heatwave prediction at the subseasonal scale in the Sahel, and gain more time and precision for anticipatory actions.

Drought is a complex natural hazard that can occur in any climate and affect every aspect of society. To better prepare and mitigate the impacts of drought, various indicators can be applied to monitor and forecast its onset, intensity, and severity. Though widely used, little is known about the efficacy of these indicators which restricts their role in important decisions. Here, we provide the first validation of 11 commonly-used drought indicators by comparing them to pasture and browse condition data collected on the ground in Kenya. These ground-based data provide an absolute and relative assessment of the conditions, similar to some of the drought indicators. Focusing on grass and shrublands of the arid and semi-arid lands, we demonstrate there are strong relationships between ground-based pasture and browse conditions, and satellite-based drought indicators. The soil adjusted vegetation index has the best relationship, achieving a mean r 2 score of 0.70 when fitted against absolute pasture condition. Similarly, the 3-month vegetation health index reached a mean r 2 score of 0.62 when fitted against a relative pasture condition. In addition, we investigated the Kenya-wide drought onset threshold for the 3-month average vegetation condition index (VCI3M; VCI3M < 35), which is used by the country’s drought early warning system. Our results show large disparities in thresholds across different counties. Understanding these relationships and thresholds are integral to developing effective and efficient drought early warning systems. Our work offers evidence for the effectiveness of some of these indicators as well as practical thresholds for their use.