As a comprehensive extension of the previous multimodel/multianalysis superensemble (SE) studies of rainfall forecasts, the benefits and prospects of the SE precipitation forecasts are explored using satellite products. Three different precipitation ensemble configurations are first established from a great number of numerical experiments. These configurations are multianalysis (MA), multicumulus-scheme (MC), and multimodel (MM) configurations. A set of MA ensemble comes from the use of several different satellite-derived rain rates through the physical initialization procedure within the Florida State University Global Spectral Model (FSUGSM) system. Six different state-of-the art cumulus parameterization schemes are incorporated into the FSUGSM in order to introduce the MC ensemble configuration. The MM configuration is composed of an FSU control forecast and those provided by five operational numerical weather prediction centers. In addition to the original technique, a possible deterministic SE enhancement technique (regression dynamic linear model) is then proposed and applied to the above three configurations of ensemble members as well as all of them together. The impact of a higher resolution family of models on the performance of SE forecasts is extensively investigated by repeating the above procedure with T170 resolution precipitation forecasts. Results show that short- to medium-range SE forecasts are generally superior in skill to various conventional forecasts. A notably improved (~20%) quantitative precipitation forecast is exhibited by the newly proposed SE technique. The MM configuration proved to be the most effective ensemble prediction system. Although a higher-resolution SE forecast requires a large amount of computing time, it turns out that the impact is significant not only in skill scores but also in resolving mesoscale-based convective disturbances.