It is widely known that precipitation estimates that are produced from passive microwave instrumentation are clearly superior to estimates derived from infrared (IR) sensors. The reason is that microwave observations detect information that is more physically related to precipitation than is possible from infrared data. However, because of the antenna size considerations due to the microwave frequencies involved, these instruments currently reside on low altitude, polar or other non-geostationary orbiting platforms, thus temporal sampling is a major drawback toward the use of these data for the production of near real-time and spatially complete global precipitation analyses. Infrared data on the other hand, while inferior to passive microwave data for precipitation estimation, are available globally nearly everywhere, nearly all the time. An obvious solution is to meld the two diverse data types to yield a hybrid in which the accuracy of the passive microwave data is combined with the excellent sampling that the IR data afford. Several products have been developed and published in which the IR data are used in a statistical fashion to mimic microwave estimates to fill in the gaps when microwave data are not available. We present a technique in which passive microwave-derived precipitation features are propagated by IR data. The resulting fields are then "morphed" by linearly interpolating between the most recent microwave observations, which modifies the shape and intensity of the precipitation features that have been propagated by IR. This process yields spatially and temporally complete passive microwave-derived precipitation analyses at time and space scales that are potentially amenable to hydrometeorological applications.