The skill of the diverse-based precipitation products is investigated in comparison with the observed-derived HYBAM and with GoAmazon and TRMM-LBA field campaigns data. The performance of eight remote sensing-based datasets (CHIRPS, MSWEP, TRMM, CMORPH, IMERG, PERSIANN-CDR, PERSIANN-CCS-CDR, and PERSIANN-CCS) is evaluated from 1998 to 2009 considering different timescales (diurnal, intraseasonal, and seasonal) for the Amazon Basin (AB). To compare the rainfall products, we applied cluster analysis, the Seasonality Index, the Kling-Gupta Efficiency metric, categorical indices, spectral analysis, and composing technique. Overall, the databases poorly represent the precipitation in the northwest of the AB (NWAB), a region with a lack of in situ measurements. CHIRPS underestimates NWAB precipitation at the seasonal scale, while at the intraseasonal timescale most databases do not adequately represent the precipitation anomalies in the NWAB, except for MSWEP. The PERSIANN-CCS-CDR product overestimates precipitation and extreme rainfall, while CMORPH overestimates the number of no rain events. At the diurnal timescale, most databases overestimate nighttime precipitation and underestimate it in the afternoon. Although there is no single database more suitable for the AB at all timescales, CHIRPS, MSWEP, and PERSIANN-CDR are the most accurate databases at seasonal timescale, while at the intraseasonal scale MSWEP would be the most appropriate. The estimates that combine reanalysis data, surface measurements, and infrared and microwave satellite data through more sophisticated techniques, such as artificial neural networks, can improve the representation of the rainfall in the AB, especially at the diurnal timescale.