In this DTC-sponsored study we explore a method to improve the predictability of cold season precipitation type for the operational High-Resolution Rapid Refresh Model Time-Lagged Ensemble (HRRR-TLE) through application of unique postprocessing techniques. TLEs are a computationally efficient method for improving probabilistic forecasts as the differences between model runs can provide an additional measure of initial condition uncertainty. Here, given that precipitation will occur, we apply evolutionary programming using HRRR-TLE forecast inputs for each of the three time lags to construct logistic regression equations calculating the probability of rain (pRN), probability of mixed precipitation (pMX), probability of freezing rain (pZR), probability of ice pellets (pIP), and the probability of snow (pSN). These equations are derived for 5 regions from 100°W eastward across the CONUS. These probabilities are then bias corrected using a decaying average process and optimal weights for each time-lagged ensemble member are developed using Bayesian Model Combination (BMC). These forecasts provided enhanced probabilistic information for both the areal distribution of cold season precipitation and the timing and location for precipitation phase transitions.