The usefulness of satellite multisensor precipitation products such as NASA’s 30-minute, 0.1° Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG) is hindered by their associated errors. Reliable estimates of uncertainty would mitigate this limitation, especially in near-real time. Creating such estimates is challenging, however, due both to the complex discrete-continuous nature of satellite precipitation errors and to the lack of “ground truth” data precisely in the places—including complex terrain and developing countries—that could benefit most from satellite precipitation estimates. In this work, we use swath-based precipitation products from the Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR) as an alternative to ground-based observations to facilitate IMERG uncertainty estimation. We compare the suitability of two DPR derived products, 2ADPR and 2BCMB, against higher-fidelity Ground Validation Multi-Radar Multi-Sensor (GV-MRMS) ground reference data over the contiguous United States. 2BCMB is selected to train mixed discrete-continuous error models based on Censored Shifted Gamma Distributions. Uncertainty estimates from these error models are compared against alternative models trained on GV-MRMS. Using information from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, we also demonstrate how IMERG uncertainty estimates can be further constrained using additional precipitation-related predictors. Though several critical issues remain unresolved, the proposed method shows promise for yielding robust uncertainty estimates in near-real time for IMERG and other similar precipitation products at their native resolution across the entire globe.