## 5 Conclusions  
The objective of the study was to compare precipitation estimates based on remote sensing from GPM, TRMM, and GPCP; and reanalysis from GLDAS and MERRA productswith measurements from conventional weather stations over the state of Mato Grosso. In general, temporally, both remote sensing and reanalysis precipitation products have satisfactorily estimated the monthly and much of the annual amounts. However, at daily time scales, some of these products showed low correlations with the measurements from. Precipitation estimates by the remote sensing products from GPM, TRMM, and GPCP had better performance compared to those based on reanalysis from GLDAS and MERRA. Precipitation products from GPM have an advantage over those from TRMM and GPCP. The performance of GPM and TRMM products, from high to low, was followed by those from the GPCP and GLDAS while MERRA showed the lowest performance. Spatially, precipitation estimates by TRMM, GPM, GPCP, and GLDAS showed higher correlation coefficients, from high to low, and lower errors, from low to high, respectively within the Amazon biome in the northern region than in the Cerrado and Pantanal in the southern regions of Mato Grosso. In contrast, MERRA estimates showed lower correlation and higher errors in all regions of the state. These findings, though, should be viewed along with the fact that the northern part of the state is represented by large scale tropical precipitation and cloud systems with less spatial variability that make it easy to estimate precipitation. These systems gradually become small scale scattered and more spatially variable clouds towards the southern regions as the climate eventually transition to subtropical when moving more south. While the northern region was represented with low density measurements stations, the southern regions were represented with relatively higher measurements density but not high enough to capture the spatial variability of these small-scale cloud systems. This combination should explain the performance of these precipitation estimates from north to south of Mato Grosso. This study demonstrated which remote sensing and reanalysis products can best serve as an alternative in the absence of rain gauge measurements. The findings of this assessment are expected to aid research and applications that require this type of precipitation data in Mato Grosso including sustainable agricultural production, hydrological analysis, water resources management, and drought management. Finally, this study also indicated that it is important to increase the number of weather stations to allow for a more spatially representative evaluation of precipitation estimates and measurement. Conceptualization, A.L.P.J., M.S.B., N.G.M., G.L.V. and H.M.E.G. ; methodology, A.L.P.J., M.S.B., N.G.M. and C.A.S.Q. ; software, A.L.P.J. and M.S.B. ; validation, A.L.P.J., M.S.B., N.G.M., L.O.F.d.S., H.M.E.G. and N.L.N. ; formal analysis, A.L.P.J., M.S.B., N.G.M., G.L.V., H.M.E.G., I.O.I. and N.L.N. ; investigation, A.L.P.J. and M.S.B. ; resources, A.L.P.J. ; data curation, A.L.P.J. and A.L.P.J. ; writing--original draft preparation, A.L.P.J., M.S.B. and N.G.M. ; writing--review and editing, M.S.B., N.G.M., G.L.V. and H.M.E.G. ; visualization, M.S.B. and H.M.E.G. ; supervision, M.S.B. ; project administration, M.S.B. ; funding acquisition, M.S.B. All authors have read and agreed to the published version of the manuscript. This research was partially funded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), code #407463/2016-0, #310879/2017-5 and #305761/2018-8, Fundacao de Amparo a Pesquisa do Estado de Mato Grosso (FAPEMAT), code #561397/2014, Universidade Federal de Mato Grosso (UFMT), Programa de Pos-Graduacao em Fisica Ambiental (PPGFA/IF/UFMT), Instituto Federal de Mato Grosso (IFMT), the National Science Foundation (NSF) Award Number IIA-1301346, and New Mexico State University. Informed Consent Statement: Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study.