## 1 Introduction  
The state of Mato Grosso (MT) has the third largest territorial area among the Brazilian states [1]. The economy of Mato Grosso is strongly dependent on agrobusiness production [2; 3]. The agricultural activities in the state is characterized by increased production of rain-fed pasture (for livestock) and soybean [4]. Within a 20-year period, cropland of soybean increased by about 275% [3]. Based on 2017 estimates, Mato Grosso was considered the largest producer of soybean as it accounted for \(\sim\)28.9% of all production in Brazil. The sustainability rainffed agricultural production and water resources is therefore affected by the spatial and temporal distribution of precipitation [3; 4]. Similarly, precipitation plays a key role in evaluating drought conditions [5], streamflow forecasting [6], and regional water balance [7]. In common, these activities require adequate knowledge of precipitation dynamics [4]. For agricultural production, monitoring precipitation is essential to characterize agricultural zoning, crop planting, irrigation depth (requirements), and harvesting periods in MT [8; 9; 10]. The precipitation in MT varies, on average, from 1200 to 2200 mm year\({}^{-1}\)[8]. This spatial variation occurs due to the state's geographical location in the central region of South America, where intertropical and extratropical systems interact [11]. The highest amounts of precipitation occur in the northern part of the state, followed by the central, eastern, and southern regions, and are associated with the development of equatorial fronts that emerges from the Amazon during the rainy season [8]. Winter precipitation is influenced by cold fronts that influence the dry season in the state [12]. In addition to macroscale phenomena, the biophysical characteristics of the surface, such as vegetation and geographic relief, also influence the distribution of precipitation in the state [8]. However, the current spatial distribution of the weather stations in Mato Grosso is not geographically representative of the entire extent of the state, and the accessibility to the data is also limited due to the high number of measurement failures from methodological, technical, and geographic issues [13]. Precipitation estimates in places where there is no weather station can be obtained using several other alternative methods, among them, reanalysis products such as those from the Global Land Data Assimilation (GLDAS) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), and remote sensing products such as those from the Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP). However, these products have their own advantages and disadvantages. The reanalysis methods can provide precipitation estimates at relatively high spatial resolution and represent the precipitation processes in synoptic scale properly, but recent findings suggested that they tend to have a high level of uncertainty when mesoscale convective regimes occur [14]. On the other hand, remote sensing products provide relatively better estimates but their estimates can be affected by the characteristics of the terrestrial surface and the microphysics of clouds, which may confuse high-level (cold) cloud tops with precipitation occurrence [15]. The socioeconomic of Mato Grosso is dependent on providing highly accurate spatial and temporal distribution of precipitation. Taking advantage of the several currently available precipitation products, it is important to provide an assessment and improved understanding of how these products are able to represent the spatial and temporal variability precipitation in the region. The objective of this study was to compare the precipitation estimated by the GLDAS, MERRA, TRMM, GPM, and GPCP products with ground observations in Mato Grosso. The analysis evaluated the performance of these products at daily, monthly, and annual time scales as well as spatially over Mato Grosso.