To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting (WRF) model at convection-permitting resolution. This study spans a two-month period during the wet seasons of 2014 and 2015. We observe a strong correlation between MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥ 10 mm h-1) and longer traveled distances than observed. These biases in simulated MCS properties vary with the strength of constraints on convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A relevant case study for April 1, 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in convective precipitation features.