Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time.