Accurate areal rainfall estimation is essential for hydrological modeling and flood forecasting, yet method selection remains uncertain in small catchments with extremely sparse gauge networks. This event-scale study compared arithmetic mean (AM), Thiessen polygon (TP), inverse distance weighting (IDW), precipitation–elevation linear regression (ELR), multiple linear regression (MLR), and a multi-layer perceptron (MLP) in a 0.719 km² catchment monitored by three gauges. Two complementary evaluations were conducted. Station-wise leave-one-out cross-validation (LOOCV) assessed prediction at an omitted gauge, whereas a radar-referenced comparison assessed catchment-average estimates obtained from the complete gauge network. MLR produced the lowest LOOCV error (RMSE = 0.433 mm; CC = 0.777). In the radar comparison, MLP and MLR produced nearly identical RMSE values of 0.668 and 0.669 mm, respectively, and are therefore interpreted as practically similar rather than meaningfully different. All method rankings are conditional on the selected 60 h event, the three-gauge arrangement, and uncertainty in the radar reference. The findings demonstrate that station-omission performance and full-network areal estimation address different operational questions and should be considered together when selecting an interpolation method for extremely sparse networks.