Quantifying the distribution of mountain snow is critical issue to ensure effective water resource management, skillful hydrological prediction, and hazard mitigation. This manuscript presents a highly impactful framework for quantifying meteorological forcing uncertainty in model simulations of mountain snow using novel machine learning applications and large sample emulators. By strictly adhering to FAIR data principles and utilizing open-source, automated workflows, the study provides a compelling demonstration of best practices in hydrological modeling. This paper will serve as an invaluable guide for early-career hydrologists seeking practical examples of rigorous code development and modeling workflows.