Quantitative precipitation estimation (QPE) in mountainous regions remains a challenging task owing to its high spatiotemporal variability. Satellite-based radar observations at high resolution have the best potential to capture the spatial patterns of precipitation, but there is high uncertainty in the interpretation of low-level measurements due to ground clutter effects, observing geometry, and sub-grid scale vertical and horizontal heterogeneity of precipitation systems that result from interactions among orographic clouds and propagating storm systems. In the high elevation tropics and in middle mountains everywhere, the landscape is often immersed in multi-layered cloud systems that modify precipitation significantly at low levels in a complex manner depending on time of day and location very different from the classical understanding of orographic precipitation enhancement with elevation, and are not easily parameterized or corrected for in QPE algorithms. Here, a review of challenges to remote sensing of orographic precipitation with a focus on the physical-basis of rainfall estimation errors is presented using radar measurements and precipitation products from the Tropical Rainfall Measurement Mission (TRMM) and Global Precipitation Measurement (GPM) satellites, ground-validation (GV) data in the Andes, the Himalayas, and the Southern Appalachian Mountains, and model simulations. Emphasis is placed on spatial and temporal variability of rainfall and associated cloud systems with a focus on water cycle research and hydrological applications in the tropics and in the mid-latitudes.