## 4 Conclusions
The SPPs evaluated were: CHIRP v2.0, CHIRPS v2.0, 3B42 RT v7.0 uncalibrated, 3B42 RT v7.0, GSMAP-NRT-no gauges v6.0, GSMAP-NRT-gauges v6.0, CMORPH v1.0 RAW, CMORPH v1.0 CRT, PERSIANN CDR, CoSch, and TAPEER v1.5, using data from the rain gauges of INPE's database as the reference. Cases for this study were chosen based on the most extreme values of precipitation observed in the studied time series. They represent the most extreme events in the study period, 2012-2016. By construction, we selected one extreme event for each region in Brazil in each time series. Analyzing more cases would be a great way to obtain climatology; however, that is beyond the scope of our analysis, which was limited to the most extreme events. Analyzing several events with similar characteristics within a region would provide valuable insights into intense events (e.g., events above a pre-defined threshold). Still, it would not be an analysis of extremes. Another valuable step, if data were available, would be to analyze double-polarized radar data to obtain a physical characterization of storms, including different types of hydrometeors, the amount of ice, and electrical activity in extreme rainfall events. Conceptualization, R.P. ([PERSON]) and D.V. ; methodology, R.P. ([PERSON]); software, R.P. ([PERSON]) and D.R. ; validation, R.P. ([PERSON]); formal analysis, R.P. ([PERSON]); investigation, R.P. ([PERSON]); resources, R.P. ([PERSON]); data curation, R.P. ([PERSON]); writing--original draft preparation, R.P. ([PERSON]) and R.P. ([PERSON]); writing--review and editing, R.P. ([PERSON]), E.M., and E.A.U. ; visualization, R.P. ([PERSON]) and D.R. ; supervision, D.V. ; project administration, R.P. ([PERSON]) and D.V. ; funding acquisition, D.V. and E.A.U. All authors have read and agreed to the published version of the manuscript. This study was financed in by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) and Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brazil (CAPES)--Finance Code 001. In addition, the authors would like to thank the Scientific and Technological Centre of Valparaiso under grant ANID PIA/APOYO AFB180002, based at the Universidad Tecnica Federico Santa Maria and Fondecyt de l'Iniciacion a Investigacion Grant No. 11190068 from the Agencia Nacional de Investigacion y Desarrollo (ANID) and Research Center for Integrated Disaster Risk Management (CIGIDEN), ANID/FONDAP/15110017. Not applicable. Data Availability Statement: The database ([PERSON] et al., 2019) is referenced with the following DOI: [[https://doi.org/10.14768/06337394-73A9-407C-9997-0E380](https://doi.org/10.14768/06337394-73A9-407C-9997-0E380) DAC5598]([https://doi.org/10.14768/06337394-73A9-407C-9997-0E380](https://doi.org/10.14768/06337394-73A9-407C-9997-0E380) DAC5598), accessed on 15 July 2022. The DOI landing page provides the up-to-date information on how to access the database as well as a number of useful references for users. The authors express their sincere thanks to the scientists responsible for the development of satellite algorithms and to Laboratoire d'Etudes en Geophysique et Oceanographie Spatiales (LEGOS/CNES/CNRS) for providing FROGS/TOOCAN dataset. They also acknowledge the INPE for the rain gauge data database utilized in this study. The authors declare no conflict of interest.