Advancing March-May seasonal rainfall prediction for the East Africa region through Machine Learning to facilitate agriculture management

Accurate weather and climate predictions are crucial for agriculture, water management, and disaster preparedness across Africa. However, several studies have highlighted the need to improve rainfall prediction at short-range, medium-range, sub-seasonal, and seasonal timescales. The inability of numerical weather prediction models to reliably capture probabilities of near-normal rainfall, coupled with their overconfidence, poses a significant challenge for many operational weather and climate prediction centers in Africa.

To address these challenges, we developed a machine learning (ML)-based framework for March–May (MAM) seasonal rainfall forecasting within East Africa Region, utilizing Random Forest (RF) and Extreme Gradient Boosting (XGB) models. These models leverage key climatic indicators, including the Indian Ocean Dipole (IOD), Mozambique Channel Trough (MOZ), and Oceanic Niño Index (ONI), computed as lagged indices (December–January) to capture antecedent conditions driving seasonal rainfall. About fifteen climate drivers computed from winds, soil moisture and sea surface temperatures were used as inputs for the machine learning models outputting MAM seasonal total rainfall.

Feature selection using mutual information scoring identified predictors with the strongest relationships to rainfall variability. Separate ML models were developed for each IGAD country to account for the spatial heterogeneity of climatic drivers, ensuring localized precision. A fair forecast performance of the RF and XGB models was achieved so far and also offering advantages in handling complex non-linear relationships.

This study demonstrates the potential of integrating ML with traditional forecasting methods to address the limitations of current model products, providing improved predictions to inform disaster risk reduction and climate adaptation strategies. By advancing the understanding of rainfall drivers, this work supports actionable decision-making for climate resilience within the East Africa region.