Precipitation is one of the most crucial components of the hydrological cycle. Precise and accurate spatial and temporal assessment of precipitation is needed for various fields such as water resources systems, agricultural practices, climatology, hydro-energy, etc. Daily rainfall data serves as a pivotal and highly sought-after input in water resources studies. Yet, it faces significant challenges due to the often-low density and subpar quality of in-situ observations. Traditionally, the precipitation over the region is estimated from point-based rain gauge measurements, which are interpolated to display the areal precipitation. However, information from the rain gauge networks usually underestimates precipitation and has poor spatial coverage. To overcome this challenge, satellite-based precipitation is used to provide rainfall estimates with high spatio-temporal resolution over large regions. However, this data is often marred by substantial errors, particularly when considered at a daily temporal resolution. Consequently, there is a pressing need for effective methods and protocols for downscaling, validating, and bias-correcting rainfall data. The primary objective of this study is to validate the downscaled satellite-derived daily rainfall dataset by comparing it with in-situ observations. This validation process is instrumental in further merging the downscaled datasets with the in-situ observations, ultimately enhancing their accuracy. Subsequently, an evaluation is carried out to identify the superior-performing dataset. In the present study, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) precipitation estimations were analyzed and compared with station-based precipitation measurements at daily, monthly, and yearly time scales. To compare the performance of the satellite with the rain gauge observations for a small agricultural watershed in Tadepalligudem, India, a contingency matrix and statistical and categorical validation measures are used. The agricultural watershed has an area of 5375 ha and receives most of its rainfall from July to October from the southwest monsoons. The watershed receives annual rainfall of 850–950 mm. The statistical and categorical evaluations help to avoid uncertainties that would arise from either underestimation of rainfall estimates or overestimation of satellite data. The results of this study help in analyzing the role of satellite precipitation datasets, their use, and their reliability for various hydro-climatological and hydrological models in small agricultural watersheds.