Abstract: The use of satellite precipitation products (SPP) allows for precipitation data to be collected globally, but questions remain regarding their ability to reproduce extreme precipitation over mountainous terrain. In this work, we assess the ability of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) to capture daily precipitation extremes by comparing PERSIANN-CDR versus corresponding station data in the summer at remote locations in the northern US Rocky Mountains of Wyoming, Idaho, and Montana. Our procedure utilizes the regular variation framework from extreme value theory (EVT), and consists of two distinct approaches. We first assess PERSIANN-CDR’s ability to model precipitation extremes through inference on an extremal dependence parameter. We also investigate the degree to which elevation and topographic heterogeneity impact the level of asymptotic dependence between these data sources over our study region. Finally, through the development of a unique modeling approach, we aim to identify the degree to which meteorological factors may contribute to asymptotic dependence between these two data products.