Articles | Volume 26, issue 3
https://doi.org/10.5194/nhess-26-1269-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-1269-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September–November rainfall events over Equatorial Africa
Hermann Ngueyon Nana
CORRESPONDING AUTHOR
Laboratory for Environmental Modelling and Atmospheric Physics (LEMAP), Physics Department, University of Yaounde 1, P.O. Box 812, Yaounde, Cameroon
Roméo Stève Tanessong
Department of Meteorology and Climatology; Higher Institute of Agriculture, Forestry, Water and Environment, University of Ebolowa, P.O. Box 118, Ebolowa, Cameroon
Laboratory for Environmental Modelling and Atmospheric Physics (LEMAP), Physics Department, University of Yaounde 1, P.O. Box 812, Yaounde, Cameroon
Masilin Gudoshava
IGAD Climate Prediction and Applications Centre (ICPAC), Nairobi, Kenya
Derbetini Appolinaire Vondou
Laboratory for Environmental Modelling and Atmospheric Physics (LEMAP), Physics Department, University of Yaounde 1, P.O. Box 812, Yaounde, Cameroon
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The results of this study show that extreme rainfall in November 2023 over Equatorial Africa was controlled by several factors, including strong sea-surface-temperature anomalies in the Niño-3.4, North Tropical Atlantic, Equatorial Atlantic and Indian Ocean Dipole regions; changes in zonal winds; the Walker circulation; the moisture flux and its divergence; and the easterly jets. The information we derive can be used to support risk assessment in the region and to improve our resilience to ongoing climate change.
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Short summary
The results of this study reveal that the seasonal forecast model used here successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over the region for both September and August initial conditions, with notably better skills for September, compared to August. In addition, the model effectively captures the teleconnections between rainfall and tropical sea surface temperature, including the Indian Ocean dipole and El Niño-Southern Oscillation.
The results of this study reveal that the seasonal forecast model used here successfully...
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