Several algorithms that combine daily precipitation surface data and satellite Climate Prediction Center Morphing Technique (CMORPH) estimations were implemented and tested for the Rio Negro basin in northeastern Uruguay. Bias removal of satellite data through quantile matching—which requires historical data on nearby rain gauges—produces an unbiased estimate whose skill, as measured by the probability of detection (POD), is better than that obtained from surface observations for distances larger than approximately 50 km, which is twice the network characteristic distance between gauges of 23 km. Adjustment of satellite estimate using spatial interpolation of CMORPH deviations evaluated at nearby points—which requires simultaneous neighboring surface observations—eliminates biases to a large degree. Moreover, it shows higher POD skill than using only surface data for the entire range of distances and daily precipitation thresholds and for both seasons (cold and warm). The skill improvement attained, though, is small when the network density is as high as in the present study. However, these results suggest a promising scenario for the combined use of surface data and satellite retrievals as the latter continues to improve over time, both in resolution—spatial and temporal—and skill.