The Indus watershed is a highly populated region that contains parts of India, Pakistan, China, and Afghanistan. Changes in precipitation patterns and rates of glacial melt have significantly impacted the region in recent years, and climate change is projected to result in further serious human and environmental consequences. To understand the climate dynamics of the Indus watershed and surrounding regions, scientists use reanalysis and satellite data from sources such as MERRA-2, ERA5, TRMM, and APHRODITE, yet these products are not always in agreement regarding critical variables such as precipitation. Because these data sources are on different spatial scales, we propose a low-rank spatiotemporal dynamic linear model for precipitation that integrates information from each of the above climate products. Specifically, we model each data source as the combination of a modified shared process, a discrepancy process, and Gaussian noise. We define the shared process at a high spatial resolution that can be upscaled according to the resolution of the observed data. Our proposed model then provides a cohesive picture of monthly precipitation in the Indus watershed from 2000-2009.