## 3 Results  
### Assessment of Temporal Accuracy  
The daily precipitation estimates by the TRMM consistently overestimated the corresponding measurements by about 4.7%. Monthly and annual precipitation estimates by TRMM were not significant different from the corresponding measurements. Similarly, monthly precipitation estimates by the GPCP did not show significant difference when compared with measurements, however, the annual precipitation estimates overestimated the measurements by 7.1%. Precipitation estimates by MERRA consistently underestimated observations by an average of about 22.9% on the three-time scales (i.e., daily, monthly, and annual). The precipitation estimated by the GLDAS and GPM products did not differ significantly on all the three-time scales (daily, monthly, and annual) when compared with the measurements. A relatively similar performance by the TRMM was also observed at the monthly and annual precipitation estimates and by the GPCP at the monthly precipitation estimates (Figure 3).  
Daily precipitation estimates based on the GPM had the highest correlation and Willmott coefficients and the smallest error (r = 0.49; d = 0.68; and RMSE = 11.25 mm day\({}^{-1}\)) than those provided by all other products. On the other hand, the daily precipitation estimated by MERRA had lowest correlation and Willmott coefficients and highest error (r = 0.20; d = 0.35; and RMSE = 13.93 mm day\({}^{-1}\)) (Figure 4A).  
The monthly precipitation estimates by the GPM resulted in r = 0.93; d = 0.96; and RMSE = 45.26 mm month\({}^{-1}\) and those by the TRMM resulted in r = 0.93; d = 0.96; and RMSE = 48.75 mm month\({}^{-1}\). Both GPM and TRMM monthly estimates had higher correlation and Willmott coefficients and smaller error than those provided by the other three products (i.e., MERRA, GLDAS, GPCP). The monthly precipitation estimated by MERRA resulted in the lowest correlation and Willmott coefficients and the highest error with r = 0.76; d = 0.84; and RMSE = 92.40 mm month\({}^{-1}\) (Figure 4B).  
On an annual time scale, the performance of precipitation estimates from all products was similar to that of the monthly time scale, with highest correlation and Willmott coefficients and the lowest error provided by the GPM (r = 0.86; d = 0.90; and RMSE = 207.98 mm year\({}^{-1}\)) and TRMM (r = 0.80; d = 0.86; and RMSE = 198.44 mm year\({}^{-1}\)) (Figure 4C).  
Based on the Wilks performance indicators (Equations (4)-(7)), the daily precipitation estimates by the TRMM provided the best performance as indicated by the categorical indices with CSI = 0.49, POD = 0.57, and FAR = 0.22. However, the daily products by the GPM, TRMM, GLDAS, and MERRA tended to underestimate the occurrence of daily precipitation events, with a BIAS that ranged from 0.33 to 0.74. MERRA persistently provided an occurrence of daily precipitation events and resulted in CSI = 0.33, POD = 0.33, FAR = 0, indicating that it estimated a high number of events on days when, in reality, there were no daily precipitation events (Figure 4D).  
Figure 3: Averages values with the corresponding \(\pm\) Confidence Interval (CI) of (**A**) daily (mm day\({}^{-1}\)), (**B**) monthly (mm month\({}^{-1}\)), and (**C**) annual (mm year\({}^{-1}\)) precipitation as measured by conventional weather stations and estimated by GLDAS, MERRA, TRMM, GPM, and GPCP products in the state of Mato Grosso, Brazil. For (**A**), the daily values for the entire study period were averaged, while for (**B**), the monthly values for all months during the study period were averaged, and the annual values in (**C**) were the annual values averaged for all years during the study period.