Reanalysis-driven Prediction of Monthly Temperature Anomalies in West Africa Using Interpretable Machine Learning

In West Africa, accurate predictions of temperature are very essential for agriculture, health and energy planning, where climate change and increasing heat pose a high risk. This study develops an open and propagative pipeline for predicting monthly surface temperature anomalies using the ERA 5 Reanalysis inputs and interpretable machine-learning models. The predicting variables include land -atmosphere flux, soil moisture, radiation conditions, circulation fields, and oceanic indices, which are processed into anomalies and lagged features to capture persistence and memory. The results show that machine-learning models continues leading the climatological and persistence baselines, With the strongest gains occurring during transition seasons and over semi-arid regions where land–atmosphere coupling is strong. Interpretive analysis reveals physically relevant relationships: deficits of soil moisture operate positive anomalies through lowered cooling by evaporation; Shortwave radiation and cloud cover modulate surface energy balance; And the lagged anomalies encode land-split memory. Water-borne countries, especially the Gulf of the Guinea SST, contribute during the transitional months, but are secondary to local reactions. Case studies and sensitivity analysis confirm the strength of these mechanisms by identifying coastal gradients and strongly convection periods. The findings suggest that machine learning provides efficient and physically consistent predictions of West African temperature discrepancies, providing practical value for climatic services in agriculture, health and energy fields. The released pipelines and artifacts carry forward the route towards fertility, integration with regional institutions, and integration with dynamic forecasts, operating climate-informed decision support in the region.