<p>Spatial rainfall patterns exert a key control on the catchment scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km<sup>2</sup> catchment located in the Swiss Alps where rainfall events were monitored during 3 summer months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help predict hydrologic processes. The framework involves the definition of spatial rainfall distribution metrics based on hydrological and geomorphological considerations, and the analysis of how these metrics explain the hydrologic response in terms of runoff coefficient and lag time. The gained insights are then used to investigate the optimal raingauge network density for predicting the hydrological metrics in the studied catchment. The analysis unravels that besides amount and intensity, the rainfall distance from the outlet along the stream network is a key spatial rainfall metric. This result calls for more detailed observations of stream network expansions, as well as the parameterization of along stream processes in rainfall-runoff models. In addition, despite the small spatial scale of this case study, the results show that an accurate representation of the rainfall field is of prime importance to capture the key characteristics of the hydrologic response in terms of generated runoff volumes and delay. In the present case, at least three rain gauges were required for proper runoff prediction.</p>