###### Abstract
The Global Precipitation Measurement Core Observatory (GPM-CO) is an international satellite mission to advance our knowledge and understanding of global precipitation [17]. The GPM-CO is a collaboration between NASA and JAXA. Their main goal is to improve precipitation measurements by understanding the physics and space-time variability of precipitation across the Earth. The most advanced precipitation instrumentation currently in orbit is a Dual-frequency Precipitation Radar (DPR) operating at 13.6 GHz and 35.5 GHz and the GPM Microwave Imager (GMI), which is composed of thirteen microwave channels ranging in frequency from 10 GHz to 183 GHz. Both instruments in the GPM-CO are designed to measure from light to most intense precipitation (0.2-111 mm/h). Using information from the newest GPM-CO satellite constellation, JAXA has been improving its product, the Global Satellite Mapping of Precipitation (GSMaP). According to [PERSON] et al. [19], this product is characterized by carefully treating precipitation types in its algorithm, fully utilizing the TRMM/PR, GPM/DPR, TML, and GMI observations. GSMaP employs precipitation regime classifications and precipitation profile information constructed from the TRMM/PR, GPM/DPR, and ancillary meteorological data. A thorough classification of land, coast, and ocean surface types and utilization of land surface emissivity obtained from TRMM data are included. In addition, they mitigate severe underestimation of orographic precipitation in the original product by incorporating an orographic rainfall scheme. A recent improvement is implementing a snowfall estimation scheme that significantly extended the estimated data availability to higher latitudes. Another example relates to the efforts made by the Climate Hazards Center (CHC) at the University of California, Santa Barbara, in collaboration with the US Geological Survey and NASA SERVIR, to develop the Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) dataset. This dataset is constantly improving to reduce differences between satellite estimated data and data observed in rain stations. A publication by [PERSON] et al. [18] showed that the development team is working on a new version of the dataset, in which the assumed 'prior' distribution is allowed to change with environmental conditions. Such an approach could provide the long-term low bias performance of the current CHIRPS algorithm while allowing dry areas to receive high precipitation levels when large-scale conditions indicate strong weather disturbances. In South America, the Satellite Division and Environmental Systems of the National Center for Space Research (INPE) has been working to create a satellite product using a Combined Scheme approach (CoSch) to provide a new high-resolution, gauge-satellitebased analysis of daily precipitation [14]. This methodology is based on a combination of additive and multiplicative bias correction schemes to minimize errors of satellite estimates when compared with observed values from rain gauges. Real-time TRMM Multi-satellite Precipitation Analysis (TMPA) and Integrated Multi-satellite Retrievals for GPM (IMERG) products are used to achieve this goal. In addition to efforts to improve the MERGE product, it combines observed precipitation with satellite precipitation estimates [20]. One of the most critical factors that contribute to natural disasters is extreme rainfall. A better understanding of the physical process that causes extreme rainfall can allow rapid responses from decision-makers to lessen the impact of natural disasters on the local population. Satellite monitoring is widely used for this purpose and is essential for regions where terrestrial observations are limited or non-existent. Although satellite information is essential to monitor extreme events and potentially lessen their effects on the population, it is far from perfect. The validation of satellite products using ground instruments can help to understand their strengths and limitations.