This study investigates optimal initialization strategies for Ensemble Model Output Statistics (EMOS) in weather forecasting, with a focus on multi-scheme ensemble systems that address physical process uncertainties. While previous research has primarily focused on loss function refinement for probabilistic forecast calibration, our work examines the critical role of parameter initialization in training effectiveness. Drawing inspiration from neural network weight initialization research as source of familiar domains, we evaluate truncated normal, beta and gamma probability distributions for initializing EMOS parameters. We find that common neural network initialization techniques like He-initialization are unsuitable for EMOS due to the fundamental constraints of positive weights for ensemble members and spread estimation. Through systematic evaluation using different formulations of the continuous rank probability score (CRPS) across time windows 25, 30, 35, and 40 days, we demonstrate that sampling from a beta distribution produces superior initialization results compared to truncated normal and gamma distributions. Interestingly, the mixture model approach did not outperform alternative methodologies due to differences in training methodologies or the properties of the geographical domain. We employed the methods on WEPROG’s Multi-Scheme Ensemble Prediction System (MSEPS) for a test site at Stötten, Germany. Our findings contribute to the advancement of ensemble post-processing techniques by providing evidence-based initialization strategies that enhance the calibration of probabilistic weather forecasts irrespective of the objective function employed.