Extreme rainfall data are usually scarce due to the low frequency of these events. However, prior knowledge of the precipitation depth and return period of a design event is crucial to water resource management and engineering. This study presents a model-based selection approach associated with regional frequency analysis to examine the lack of maximum daily rainfall data in Brazil. A generalized extreme values (GEV) distribution was hierarchically fitted using a Bayesian approach and data that were collected from rainfall gauge stations. The GEV model parameters were submitted to a model-based cluster analysis, resulting in regions of homogeneous rainfall regimes. Time-series data of the individual rainfall gauges belonging to each identified region were joined into a new dataset, which was divided into calibration and validation sets to estimate new GEV parameters and to evaluate model performance, respectively. The results identified two distinct rainfall regimes in the region: more and less intense rainfall extremes in the southeast and northwest regions, respectively. According to the goodness of fit measures that were used to evaluate the models, the aggregation level of the parameters in clustering influenced their performance.