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A spatially explicit mapping of rainfall is highly required for Southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) spinning enhanced visible and infrared imager (SEVIRI). Rainfall measurements from about 350 weather stations from the years 2010-2014 served as ground truths for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed predicting rainfall area and rainfall quantities during all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (Probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to an Heidke Skill Score (HSS) of 0.18. Predicted hourly rainfall quantities were estimated with an average hourly correlation of rho = 0.33 and a RMSE of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the IMERG product of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection where GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG based retrieval, however, provides rainfall in higher spatial resolution. Though it remains challenging to estimate rainfall from satellite data, especially on a high temporal resolution, this study showed promising results towards improved spatio-temporal estimates of rainfall over Southern Africa. [ABSTRACT FROM AUTHOR]