Prediction of Malawi Rainfall from Global Sea Surface Temperature Using a Simple Multiple Regression Model

This study deals with a way of predicting Malawi rainfall from global sea surface temperature (SST) using a simple multiple regression model. Links between Malawi rainfall and SST based on statistical correlations were evaluated and selected as predictors for the model. Monthly rainfall data from nine stations in Malawi grouped into two zones on the basis of inter-station rainfall correlations were used in the study. The predictors for Zone 1 model were identified from the Atlantic, Indian and Pacific oceans while those for Zone 2 were identified from the Indian and Pacific Oceans. The correlation between the fit of predicted and observed rainfall values of the models were satisfactory with r = 0.81 and 0.54 for Zone 1 and 2 respectively (significant at less than 99.99%). The results of the models are in agreement with other findings that suggest that SST anomalies in the Atlantic, Indian and Pacific oceans have an influence on the rainfall patterns of Southern Africa. We conclude that SST in the Atlantic, Indian and Pacific Oceans is correlated with Malawi rainfall and can be used to predict rainfall values.