A cloud model–based statistical retrieval technique for estimating surface precipitation and cloud profiles over ocean, called Bayesian Algorithm for Microwave Precipitation Retrieval (BAMPR), is described. The inversion scheme, based on the Bayesian estimation theory, is trained by a CRD obtained by inputting the numerical outputs of a mesoscale microphysical model into a three-dimensional radiative transfer model. Since the performances of the retrieval are strictly dependent on the a priori information given by the CRD, the generation of the database itself, and the coupling between the forward and the inverse problem are carefully discussed. Particular emphasis is given to the database representativeness of the meteorological event under investigation and to the quantification of modeling errors. The retrieval uncertainties are provided with the estimates themselves by choosing the Minimum Mean Square technique as a Bayesian inversion method. As an example, the algorithm is applied to some case studies in the Tropics using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager data. The analysis is focused on the evaluation of the CRD performances with respect to the various events (i.e., a tropical cyclone, a tropical storm, a summer front, and some isolated convective cells in the Atolls region) and different CRDs (i.e., two hurricanes from the University of Wisconsin Nonhydrostatic Modeling System and a tropical squall line from the Goddard Cumulus Ensemble model). A detailed examination is carried out on the case of the hurricane Bonnie on 25 August 1998, which is discussed by using TRMM official products as a comparison.