||Due to their high sensitivity to changes in climate, alpine glaciers are one of the best natural indicators of climate change. Despite this, some of the underlying processes that control glacier response to climate change are not well understood. One potentially important set of such processes are feedback mechanisms that amplify and dampen melt. Though these feedbacks are widely recognized as important processes affecting glacier mass balances, little has been done to quantify their effects in a systematic way. This study develops a fully distributed surface energy and mass balance model to quantify the contributions of three precipitation-induced melt feedbacks to glacier mass balance. Specifically, we focus on feedbacks associated with sensible heat of liquid rain, snowpack thickness, and frequency of snowfall events. The model follows well-known energy balance methods, but includes "switches" that allow individual feedbacks to be turned off. The model utilizes an idealized glacier and meteorological inputs from the High Asia Refined analysis for two different climate regions in High Mountain Asia (HMA). The results show that melt feedbacks can nearly triple melt due to a +1°C temperature forcing scenario. System gains are highest near the equilibrium line altitude (ELA). Furthermore, system gains due to melt feedbacks depend most on the frequency of snowfall events that occur concurrently with the melt season. These results highlight the potential significance of melt feedbacks on glacier mass balance, how this may vary across differing climatic regions, and the need to further explore feedbacks associated with other glacier surface processes.