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Recently developed methodologies such as climate reanalysis make it possible to create a historical record of climate systems. This paper proposes a methodology called Hydrological Retrospective (HR), which essentially simulates large rainfall datasets, using this as input into hydrological models to develop a record of past hydrology, making it possible to analyze past floods and droughts. We developed a methodology for the Amazon basin, where studies have shown an increase in the intensity and frequency of hydrological extreme events in recent decades. We used eight large precipitation datasets (more than 30 years) as input for a large scale hydrological and hydrodynamic model (MGB-IPH). HR products were then validated against several in situ discharge gauges controlling the main Amazon sub-basins, focusing on maximum and minimum events. For the most accurate HR, based on performance metrics, we performed a forecast skill of HR to detect floods and droughts, comparing the results with in-situ observations. A statistical temporal series trend was performed for intensity of seasonal floods and droughts in the entire Amazon basin. Results indicate that HR could represent most past extreme events well, compared with in-situ observed data, and was consistent with many events reported in literature. Because of their flow duration, some minor regional events were not reported in literature but were captured by HR. To represent past regional hydrology and seasonal hydrological extreme events, we believe it is feasible to use some large precipitation datasets such as i) climate reanalysis, which is mainly based on a land surface component, and ii) datasets based on merged products. A significant upward trend in intensity was seen in maximum annual discharge (related to floods) in western and northwestern regions and for minimum annual discharge (related to droughts) in south and central-south regions of the Amazon basin. Because of the global coverage of rainfall datasets, this methodology can be transferred to other regions for better estimation of future hydrological behavior and its impact on society. [ABSTRACT FROM AUTHOR]