The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean-atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation.

Improved prediction, prevention, and control of epidemics is a key technical element of the Roll Back Malaria partnership. We report a methodology for assessing the importance of climate as a driver of inter-annual variability in malaria in Botswana, and provide the evidence base for inclusion of climate information in a national malaria early warning system. The relationships of variability in rainfall and sea surface temperatures (SSTs) to malaria incidence are assessed at the national level after removing the impact of non-climatic trends and a major policy intervention. Variability in rainfall totals for the period December-February accounts for more than two-thirds of the inter-annual variability in standardized malaria incidence in Botswana (January-May). Both rainfall and annual malaria anomalies in December-February are significantly related to SSTs in the eastern Pacific, suggesting they may be predictable months in advance using seasonal climate forecasting methodologies.

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