Estimating precipitation over complex terrain is especially difficult due to the small spatial and temporal scales of orographic precipitation and the lack of ground truth in steep terrain. Two areas where the various precipitation estimates diverge are the Andes and the Himalayas. Both areas have precipitation from systems varying from small showers to large Mesoscale Convective Systems, which provide differing estimation problems from a remote sensing point of view. This study will compare rain gauge data from the Global Precipitation Climatology Project (GPCP) in these areas with remote-sensing data from infrared estimates and estimates from the Tropical Rainfall Measuring Mission (TRMM) Satellite. Infrared estimates rely on a cloud-top temperature-precipitation relationship which contain large uncertainties in instantaneous rainfall estimates. Passive microwave estimates (such as those from the TRMM Microwave Imager at 85 and 37 GHz) over land correlate the optical depth of frozen hydrometeors within a cloud system to near surface rainfall, a relationship which is more directly related to rainfall than infrared estimates. However, these relationships still contain scatter due to the varying macro- and microphysical structure of precipitating systems and the presence of artifacts of elevated terrain in the data. Estimates from the world's first spaceborne radar designed for rainfall estimation, the TRMM Precipitation Radar, provide the most physically direct precipitation estimator, however issues such as sampling biases and surface echo contamination reduce the accuracy of the retrievals. Estimates from the 3-year University of Utah TRMM Precipitation Feature Database are also compared to examine the dependence of system frequency by type in the various estimates.