<p>Smallholder farmers in Sub-Saharan Africa are vulnerable to adverse fluctuations in rainfall, such as dry spells during the critical early stages of the rainy season. In this study, we demonstrate that Malawi is prone to shifts from periods with limited dry spell occurrence to more widespread dry spells later in the season. We develop a predictive model for dry spells, aiming to provide farmers with actionable information to support agricultural decision-making and enhance resilience. The model, based on a dynamical subseasonal prediction system and validated using reanalysis and satellite-based data, focuses on Malawi as a case study. This model has significant skill in predicting the occurrence of at least one dry spell within the three weeks following initialisation, consistently outperforming a climatology-based reference model. Furthermore, we show that the model is applicable beyond Malawi, specifically in East Africa during both the March–May “long rains” and the October–November “short rains”, highlighting its broader relevance for regions where dry spells pose an agricultural risk. The results demonstrate that subseasonal forecasts have the potential to bridge the gap between long-range seasonal outlooks and short-term weather forecasts. Unlike seasonal forecasts, which lack skill at long lead times, subseasonal predictions offer both a longer planning horizon than weather forecasts and greater skill in capturing dry spell risks at actionable lead times. By integrating subseasonal forecasts into national climate services, policymakers and agricultural extension services could provide more timely and targeted advice, potentially helping to mitigate the most severe impacts of dry spells on food production and rural livelihoods.</p>