Four new approaches of postprocessing quantitative precipitation forecasts (QPFs) from model ensemble output were used to generate probability of precipitation (POP) tables in order to develop a forecasting method that could outperform a traditional method that relies upon calibration of POP forecasts derived using equal weighting of ensemble members. Early warm season 10-member ensemble output from the NOAA Hazardous Weather Testbed Spring Experiments was used, with 29 cases serving as a training set to create the POP tables and 20 cases used as a test set. The new approaches use QPF–POP relationships based on two properties termed precipitation amount characteristic (PAC) and ensemble member agreement. Exploratory results are presented for 20-km grid spacing and selectively for 4-km grid spacing. In the first approach, POPs were based on a binned PAC and the number of ensemble members with 6-h precipitation accumulations greater than given thresholds. In a second approach, a neighborhood method was used to find the number of points in a given neighborhood area around each of the domain grid points with precipitation amounts greater than a given threshold, while also considering the binned PAC representative of the neighborhood. A third approach synthesized the previous methods and led to an increase in skill relative to the individual methods, and a fourth approach using a combination of methods produced forecasts with even greater skill. All of the forecasts from the four approaches were improved statistically significantly compared to the calibrated traditional method’s forecasts at 20-km grid spacing. The second approach on its own showed skill comparable to that obtained by a traditional calibrated 10-member ensemble, so adopting this approach alone could potentially save computer resources that could then be used for model refinements, only sacrificing the increased skill that could have been obtained by using the fourth approach.