The filtering effect of ensemble averaging results in an intensity bias for the ENM forecasts. Ebert (2001) proposed to correct empirically the ENM forecasts by recalibrating the intensities in the ENM using the probability density function (PDF) of rainfall values from the ensemble members. This procedure, called probability matching (PM), leads to a new ensemble mean, the Probability Matched Mean (PMM). The PMM appears more realistic and yields better skill as evaluated using traditional metrics. However, we demonstrate that despite the PMM having the same PDF of rainfall intensities as the ensemble members, the spectral structure and spatial distribution of the precipitation field differs from that of the members. It is the lesser variability of the PMM fields at small scales that causes the better scores of the PMM.
Ebert, E. E., 2001: Ability of a poor man's ensemble to predict the probability and distribution of precipitation. Monthly Weather Review, 129, 2461-2480.