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The Limpopo Basin in southern Africa is prone to droughts whichaffect the livelihood of millions of people in South Africa, Botswana,Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for thewhole region. In this study, the predictability of hydrological droughtsduring the main runoff period from December to May is assessed usingstatistical approaches. Three methods (multiple linear models, artificialneural networks, random forest regression trees) are compared in terms oftheir ability to forecast streamflow with up to 12 months of lead time. Thefollowing four main findings result from the study.  1. There are stations in the basin at whichstandardised streamflow is predictable with lead times up to 12 months. Theresults show high inter-station differences of forecast skill but reach acoefficient of determination as high as 0.73 (cross validated).  2. A largerange of potential predictors is considered in this study, comprisingwell-established climate indices, customised teleconnection indices derivedfrom sea surface temperatures and antecedent streamflow as a proxy ofcatchment conditions. El Niño and customised indices, representing seasurface temperature in the Atlantic and Indian oceans, prove to be importantteleconnection predictors for the region. Antecedent streamflow is a strongpredictor in small catchments (with median 42 % explained variance),whereas teleconnections exert a stronger influence in large catchments.  3. Multiple linear models show the best forecast skill in this study and thegreatest robustness compared to artificial neural networks and random forestregression trees, despite their capabilities to represent nonlinearrelationships.  4. Employed in early warning, the models can be used toforecast a specific drought level. Even if the coefficient of determinationis low, the forecast models have a skill better than a climatologicalforecast, which is shown by analysis of receiver operating characteristics(ROCs). Seasonal statistical forecasts in the Limpopo show promising results,and thus it is recommended to employ them as complementary to existingforecasts in order to strengthen preparedness for droughts

Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods