The launch of NOAA's latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.