The Goddard Profiling algorithm (GPROF) converts radiometer observations aboard Global Precipitation Measurement (GPM) constellation satellites to precipitation estimates. Analyzing the accuracy of GPROF’s estimates is vital to further improve the algorithm. Such analyses often use high-quality ground-based estimates as reference with a different spatial resolution. Often, the reference is resampled to match the satellite’s resolution. However, the implemented sampling method to simulate the satellite’s resolution varies amongst studies, which limits the transferability of conclusions. additionally, GPROF combines observations from various sensors and frequency channels, each with its own footprint size. Hence, uncertainties related to sampling are added on top of the uncertainty introduced when converting brightness temperatures to precipitation intensities. The contribution of sampling to the total amount of uncertainty remains unknown. Here, we quantify the uncertainty related to sampling while analyzing the current performance of GPROF over the Netherlands during a four year period (2017–2020). In this area, shallow and light precipitation frequently occur. Both precipitation types are often subject to research, as both types are difficult to detect with space-borne sensors. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. We investigate the uncertainty related to sampling by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETH) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. The method used to spatially average the reference into a satellite footprint, i.e. using Gaussian weighting or the arithmetic mean, is found to exhibit a ...