Hermann Ngueyon Nana

Accurate prediction of extreme rainfall and dry events remains a major challenge in West Africa due to the complex and non‐linear dynamics of the monsoon system, which involve interactions among local convection, large‐scale circulation, ocean‐atmosphere coupling, and intra‐seasonal variations. Here, we develop a convolutional neural network (CNN) architecture that utilizes the vertically integrated moisture flux convergence (VIMFC) as predictor to discriminate between wet, normal and dry extreme conditions. Our model demonstrates high effectiveness in identifying wet (79%) and dry (82%) events, and captures the spatiotemporal variability. Interpretability diagnostics reveal that the CNN learns physically coherent patterns of VIMFC, consistent with established West African monsoon drivers. This data‐driven approach complements traditional forecasting and highlights the promising potential of deep learning for regional climate projections in data‐scarce regions. By linking statistical learning with physical understanding of the climate, our results suggest improved forecasting capability for climate risk management in West Africa.

This study investigates the predictability of rainfall over Equatorial Africa (EA) and evaluates the forecasting performance of the European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecast version 5.1 (ECMWF-SEAS5.1) for the September–November (SON) period during 1981–2023 (43 years). The analysis considers two lead-times, focusing on initial conditions (ICs) from September and August. Regression, spatiotemporal and composite analyses are applied to highlight the relationship between extreme precipitation events over EA and the various associated atmospheric circulation drivers. The analysis reveals that ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both ICs, with notably better skills for September. In addition, the model effectively captures the teleconnections between EA rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation, for both ICs. Regions with highest potential predictability skills coincide with regions where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values, although the magnitude tends to be underestimated. However, other important observed features, such as the components of the African easterly jet, are well represented by the model for the September IC, but not for August. While many atmospheric mechanisms driving precipitation in the region are well simulated, their underestimation likely explains the model's general tendency to underestimate the magnitude of extreme rainfall events. The results of this study support efforts to improve forecast outputs in the national weather services across the region by integrating ECMWF model outputs into operational weather bulletins.

This study investigates the predictability of rainfall over Equatorial Africa (EA) and evaluates the forecasting performance of the European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecast version 5.1 (ECMWF-SEAS5.1) for the September–November (SON) periode during 1981–2023 (43 years). The analysis considers two lead-times, focusing on initial conditions (ICs) from September and August. Regression, spatiotemporal and composite analyses are applied to highlight the relationship between extreme precipitation events over EA and the various associated atmospheric circulation drivers. The analysis reveals that ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both ICs, with notably better skills for September. In addition, the model effectively captures the teleconnections between EA rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation, for both ICs. Regions with highest potential predictability skills coincide with regions where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values, although the magnitude tends to be underestimated. However, other important observed features, such as the components of the African easterly jet, are well represented by the model for the September IC, but not for August. While many atmospheric mechanisms driving precipitation in the region are well simulated, their underestimation likely explains the model’s general tendency to underestimate the magnitude of extreme rainfall events. The results of this study support efforts to improve forecast outputs in the national national weather services across the region by integrating ECMWF model outputs into operational weather bulletins.

This study investigates the predictability of rainfall over Equatorial Africa (EA) and evaluates the forecasting performance of the European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecast version 5.1 (ECMWF-SEAS5.1) for the September–November (SON) periode during 1981–2023 (43 years). The analysis considers two lead-times, focusing on initial conditions (ICs) from September and August. Regression, spatiotemporal and composite analyses are applied to highlight the relationship between extreme precipitation events over EA and the various associated atmospheric circulation drivers. The analysis reveals that ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both ICs, with notably better skills for September. In addition, the model effectively captures the teleconnections between EA rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation, for both ICs. Regions with highest potential predictability skills coincide with regions where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values, although the magnitude tends to be underestimated. However, other important observed features, such as the components of the African easterly jet, are well represented by the model for the September IC, but not for August. While many atmospheric mechanisms driving precipitation in the region are well simulated, their underestimation likely explains the model’s general tendency to underestimate the magnitude of extreme rainfall events. The results of this study support efforts to improve forecast outputs in the national national weather services across the region by integrating ECMWF model outputs into operational weather bulletins.

This study investigates the predictability of rainfall over Equatorial Africa (EA) and evaluates the forecasting performance of the European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecast version 5.1 (ECMWF-SEAS5.1) for the September–November (SON) periode during 1981–2023 (43 years). The analysis considers two lead-times, focusing on initial conditions (ICs) from September and August. Regression, spatiotemporal and composite analyses are applied to highlight the relationship between extreme precipitation events over EA and the various associated atmospheric circulation drivers. The analysis reveals that ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both ICs, with notably better skills for September. In addition, the model effectively captures the teleconnections between EA rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation, for both ICs. Regions with highest potential predictability skills coincide with regions where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values, although the magnitude tends to be underestimated. However, other important observed features, such as the components of the African easterly jet, are well represented by the model for the September IC, but not for August. While many atmospheric mechanisms driving precipitation in the region are well simulated, their underestimation likely explains the model’s general tendency to underestimate the magnitude of extreme rainfall events. The results of this study support efforts to improve forecast outputs in the national national weather services across the region by integrating ECMWF model outputs into operational weather bulletins.

This study investigates the predictability of rainfall over Equatorial Africa (EA) and evaluates the forecasting performance of the European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecast version 5.1 (ECMWF-SEAS5.1) for the September–November (SON) periode during 1981–2023 (43 years). The analysis considers two lead-times, focusing on initial conditions (ICs) from September and August. Regression, spatiotemporal and composite analyses are applied to highlight the relationship between extreme precipitation events over EA and the various associated atmospheric circulation drivers. The analysis reveals that ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both ICs, with notably better skills for September. In addition, the model effectively captures the teleconnections between EA rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation, for both ICs. Regions with highest potential predictability skills coincide with regions where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values, although the magnitude tends to be underestimated. However, other important observed features, such as the components of the African easterly jet, are well represented by the model for the September IC, but not for August. While many atmospheric mechanisms driving precipitation in the region are well simulated, their underestimation likely explains the model’s general tendency to underestimate the magnitude of extreme rainfall events. The results of this study support efforts to improve forecast outputs in the national national weather services across the region by integrating ECMWF model outputs into operational weather bulletins.

Understanding the atmospheric factors that lead to extreme rainfall events is essential to improve climate forecasting. This study aims to diagnose the physical processes underlying the extreme rainfall event of November 2023 in Equatorial Africa (EA), using the ERA5 reanalysis dataset. Composite, spatio-temporal and correlation analyses are used to shed light on the relationship between the November 2023 extreme precipitation events and the various associated factors. The analysis reveals that these extreme rainfall events were mainly controlled by several factors that occurred during this period in the Pacific, Atlantic and Indian oceans. These factors include strong sea-surface-temperature (SST) anomalies in the Niño-3.4, North Tropical Atlantic, Equatorial Atlantic and Indian Ocean Dipole (IOD) oceanic regions; changes in zonal winds; the Walker circulation; the anomalous moisture flux and its divergence; the easterly jets; and the activity of the Madden–Julian Oscillation (MJO). This convergence of moisture flows entered the EA region through its western and eastern boundaries, coming from the Equatorial Atlantic and Indian oceans, respectively. The juxtaposition of these factors has led to strong and positive rainfall anomalies in EA, with the highest values over the East African region, mainly over southern Ethiopia, Somalia, Kenya and Tanzania, which received more than 430 mm of rainfall during the month. Our findings suggest that many dynamic atmospheric effects need to be taken into account jointly to anticipate this type of extreme event. The results of the present study contribute to the improvement of sub-seasonal to seasonal rainfall forecasts by the region's national meteorological services to enable us to increase the resilience of the region's citizens to these extreme weather conditions.

Understanding the atmospheric factors that lead to extreme rainfall events is essential to improve climate forecasting. This study aims to diagnose the physical processes underlying the extreme rainfall event of November 2023 in Equatorial Africa (EA), using the ERA5 reanalysis dataset. Composite, spatio-temporal and correlation analyses are used to shed light on the relationship between the November 2023 extreme precipitation events and the various associated factors. The analysis reveals that these extreme rainfall were mainly controlled by several factors that occurred during this period in the Pacific, Atlantic and Indian oceans. These factors include strong Sea-Surface-Temperature (SST) anomalies in Niño-3.4, North Tropical Atlantic, Equatorial Atlantic and Indian Ocean Dipole (IOD) oceanic regions, changes in zonal winds, the Walker circulation, the anomalous moisture flux and its divergence, the easterly jets and the activity of the Madden-Julian Oscillation (MJO). This convergence of moisture flows entered the EA region through its western and eastern boundaries, coming from the equatorial Atlantic and Indian oceans respectively. The juxtaposition of these factors has led to strong and positive rainfall anomalies in EA, with the highest values over the East African region, mainly over southern Ethiopia, Somalia, Kenya and Tanzania, which received more than 430 mm of rainfall during this month. Our findings suggest that many dynamic atmospheric effects need to be taken into account jointly to anticipate this type of extreme event. The results of the present study contribute to the improvement of sub-seasonal to seasonal rainfall forecasts by the region's national meteorological services, to enable us to increase the resilience of the region's citizens to these extreme weather conditions.

Understanding the atmospheric factors that lead to extreme rainfall events is essential to improve climate forecasting. This study aims to diagnose the physical processes underlying the extreme rainfall event of November 2023 in Equatorial Africa (EA), using the ERA5 reanalysis dataset. Composite, spatio-temporal and correlation analyses are used to shed light on the relationship between the November 2023 extreme precipitation events and the various associated factors. The analysis reveals that these extreme rainfall were mainly controlled by several factors that occurred during this period in the Pacific, Atlantic and Indian oceans. These factors include strong Sea-Surface-Temperature (SST) anomalies in Niño-3.4, North Tropical Atlantic, Equatorial Atlantic and Indian Ocean Dipole (IOD) oceanic regions, changes in zonal winds, the Walker circulation, the anomalous moisture flux and its divergence, the easterly jets and the activity of the Madden-Julian Oscillation (MJO). This convergence of moisture flows entered the EA region through its western and eastern boundaries, coming from the equatorial Atlantic and Indian oceans respectively. The juxtaposition of these factors has led to strong and positive rainfall anomalies in EA, with the highest values over the East African region, mainly over southern Ethiopia, Somalia, Kenya and Tanzania, which received more than 430 mm of rainfall during this month. Our findings suggest that many dynamic atmospheric effects need to be taken into account jointly to anticipate this type of extreme event. The results of the present study contribute to the improvement of sub-seasonal to seasonal rainfall forecasts by the region's national meteorological services, to enable us to increase the resilience of the region's citizens to these extreme weather conditions.