1. International Research Institute for Climate and Society (IRI), The Earth Institute at Columbia University, New York, New York 10964-8000, USA
2. European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, RG2 9AX, Reading, UK
3. National Malaria Control Programme, Epidemiology and Disease Control Unit, Ministry of Health, Private Bag 00269, Gaborone, Botswana
4. Department of Geography, University of Liverpool, Liverpool, L69 7ZT, UK

Correspondence to: T. N. Palmer² Correspondence and requests for materials should be addressed to T.N.P. (Email: tim.palmer@ecmwf.int).

Abstract

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 agreed¹. Interannual climate variability is an important determinant of epidemics in parts of Africa² where climate drives both mosquito vector dynamics and parasite development rates³. 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 established⁴, 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.

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