The characteristics of the precipitation under the influence of topography and surface wind flow is investigated over South America. Here the precipitating cloud systems (PCSs) are identified using the Global Precipitation Measurement Precipitation Radar (GPM-PR) data, which provides the three dimensional radar reflectivity factor (Ze), rain rate, drop size and droplet concentration. For each PCSs the surface wind properties are estimated using European Center for Medium-Range Weather Forecast Interim data. Based on the direction of surface flow the PCSs are classified into five categories. Over the South America the near surface wind flow transports the moisture from Amazon basin to east flank of Andes and validated here. The directional surface flow decides the occurrences of the PCSs, as upslope consists of the higher and larger PCSs at the peak of Andes compared to downslope flow. The directional flow suggests that northern Andes consists of pronounced bright band characteristics compared to southern Andes, and upslope and easterly flow have higher probability of rain at the eastern slope of Andes compared to westerly and downslope flow in northern Andes. The results show that orography also modulates the precipitation characteristics under different directional flow over and near the Andes. Eastern slope of Andes has higher rain rate compared to western slope of Andes in most of the directional flow. Orographically forced moisture loaded flow, over the eastern slope of Andes causing the higher rain rate, drop radius and droplet concentration in northern Andes. At the low lands, effective drop radius and droplet concentration show the opposite characteristics, and effective drop radius (concentration) is least (highest) except for the downslope flow over the northern Andes. The results of DSD parameters along with rainfall intensity show the microphysical evolution of the precipitation under the complex orography over the Andes mountain. The present study suggests that in future, surface flow must be considered for studying the orographic precipitation in numerical modeling. © 2019