Nana et al. (2026) Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September–November rainfall events over Equatorial Africa

Identification

• Journal: Natural hazards and earth system sciences
• Year: 2026
• Date: 2026-03-10
• Authors: Hermann Ngueyon Nana, Roméo S. Tanessong, Masilin Gudoshava, Derbetini A. Vondou
• DOI: 10.5194/nhess-26-1269-2026

Research Groups

• Laboratory for Environmental Modelling and Atmospheric Physics (LEMAP), Physics Department, University of Yaounde 1, Yaounde, Cameroon
• Department of Meteorology and Climatology; Higher Institute of Agriculture, Forestry, Water and Environment, University of Ebolowa, Ebolowa, Cameroon
• IGAD Climate Prediction and Applications Centre (ICPAC), Nairobi, Kenya

Short Summary

This study assesses the European Centre for Medium-Range Weather Forecasts seasonal prediction system 5.1 (ECMWF-SEAS5.1) in forecasting extreme September–November (SON) rainfall events over Equatorial Africa (EA). It finds that the model generally reproduces observed rainfall patterns and teleconnections with tropical sea surface temperatures well, with better skill for September initial conditions, but tends to underestimate the magnitude of extreme events and shows limitations in representing certain atmospheric features at longer lead-times.

Objective

• To investigate the predictability of rainfall over Equatorial Africa (EA) and evaluate the forecasting performance of the ECMWF-SEAS5.1 model for extreme September–November (SON) rainfall events during 1981–2023, considering two lead-times (initial conditions from September and August).
• To explicitly examine the large-scale physical mechanisms (SST anomalies, moisture transport, zonal and Walker circulations) that accompany extreme rainfall events and assess the model's ability to capture these drivers.

Study Configuration

• Spatial Scale: Equatorial Africa (8–50° E; 10° S–10° N) and global oceanic regions for teleconnections.
• Temporal Scale: September–November (SON) season over the period 1981–2023 (43 years), with monthly mean data. Two lead-times are considered: initial conditions (ICs) from 1 September (L0) and 1 August (L1).

Methodology and Data

• Models used:
  • ECMWF seasonal prediction system 5, version 5.1 (ECMWF-SEAS5.1) re-forecast data (25 ensemble members).
• Data sources:
  • Observed Precipitation: Climate Hazards Group Infrared Precipitation with Station data (CHIRPS; Funk et al., 2015) at 0.25° × 0.25° horizontal grid spacing.
  • Observed Sea Surface Temperature (SST): Extended Reconstructed SST, version 5 (ERSSTv5; Huang et al., 2017) at 2° × 2° resolution.
  • Reanalysis Data (for atmospheric circulation validation): Fifth generation of European Re-Analysis (ERA5; Hersbach et al., 2020) at 0.25° × 0.25° horizontal grid spacing and 37 pressure levels.
• Methods:
  • Regression analysis to highlight relationships between extreme precipitation and atmospheric circulation drivers.
  • Spatiotemporal analysis.
  • Composite analysis for strong and weak rainfall events.
  • Potential Predictability (PP) estimation (ratio of external to internal variance).
  • Calculation of NiñO 3.4 index (N34) and Dipole Mode Index (DMI).
  • Probability Density Function (PDF) based on Gamma distribution for rainfall variability.
  • Analysis of moisture flux convergence (∇ ⋅ (qV)) and its components (moisture convergence q∇ ⋅ V and moisture advection V ⋅ ∇q).
  • Identification of extreme rainfall years using an EA rainfall Index (EAI) exceeding ±0.5 standard deviation.
  • Statistical significance testing using a two-tailed Student's t-test at a 5% level.

Main Results

• ECMWF-SEAS5.1 successfully reproduces the observed annual precipitation cycle and seasonal spatial pattern of rainfall over EA for both September (L0) and August (L1) initial conditions, with better skills for L0.
• The model effectively captures teleconnections between EA rainfall and tropical sea surface temperatures, including the Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO), for both L0 and L1.
• Regions with the highest potential predictability skills coincide with areas where the model accurately represents strong (weak) composite rainfall anomalies, associated with strong (weak) moisture flux convergence (divergence) values.
• The model tends to underestimate the magnitude of extreme rainfall events, particularly during weak years.
• For September ICs (L0), the model captures 85.71% of strong rainfall years and 83.3% of weak years. For August ICs (L1), it captures 85.71% of strong years but only 33.3% of weak years.
• Important observed features like the African Easterly Jet (AEJ) components are well represented by the model for September IC (L0) but not for August IC (L1), where the AEJ-N core is displaced southward and AEJ-S is absent.
• Moisture convergence is identified as the main component of moisture flux convergence over EA, and the model captures this well.

Contributions

• Provides an updated and comprehensive assessment of the latest ECMWF seasonal forecasting system (SEAS5.1) for extreme SON rainfall over Equatorial Africa, addressing gaps in previous evaluations that focused on earlier versions or other seasons.
• Explicitly examines the large-scale physical mechanisms (SST anomalies, moisture transport, atmospheric circulation patterns like AEJ) driving extreme rainfall, offering a more physically grounded evaluation than prior studies.
• Highlights the model's strengths in reproducing teleconnections and general rainfall patterns, particularly for shorter lead-times, and identifies specific limitations such as underestimation of extreme event magnitudes and misrepresentation of certain atmospheric features (e.g., AEJ at longer lead-times).
• Offers valuable guidance for national weather services and policymakers in the region to improve forecast outputs and strengthen adaptation strategies by integrating ECMWF model outputs into operational weather bulletins.

Funding

• International Joint Laboratory Dynamics of Terrestrial Ecosystems in Central Africa (IJL DYCOCA/LMI DYCOFAC) initiative.

Citation

@article{Nana2026Assessing,
  author = {Nana, Hermann Ngueyon and Tanessong, Roméo S. and Gudoshava, Masilin and Vondou, Derbetini A.},
  title = {Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September–November rainfall events over Equatorial Africa},
  journal = {Natural hazards and earth system sciences},
  year = {2026},
  doi = {10.5194/nhess-26-1269-2026},
  url = {https://doi.org/10.5194/nhess-26-1269-2026}
}