The aim of this study was to assess the accuracy of different methods of forecasting malaria incidence from historical morbidity patterns in areas with unstable transmission. We tested five methods using incidence data reported from health facilities in 20 areas in central and north-western Ethiopia. The accuracy of each method was determined by calculating errors resulting from the difference between observed incidence and corresponding forecasts obtained for prediction intervals of up to 12 months. Simple seasonal adjustment methods outperformed a statistically more advanced autoregressive integrated moving average method. In particular, a seasonal adjustment method that uses mean deviation of the last three observations from expected seasonal values consistently produced the best forecasts. Using 3 years' observation to generate forecasts with this method gave lower errors than shorter or longer periods. Incidence during the rainy months of June-August was the most predictable with this method. Forecasts for the normally dry months, particularly December-February, were less accurate. The study shows the limitations of forecasting incidence from historical morbidity patterns alone, and indicates the need for improved epidemic early warning by incorporating external predictors such as meteorological factors.