Amazon Basin deforestation has been driven by agricultural expansion and other developments. Interactions between deforestation, fire, climate, and drought have led to changes in precipitation patterns and river discharge. Previous work suggests that these changes are amplified by land use. Therefore, it is important to understand the degree of change in precipitation patterns and rainy season characteristics. We used long term daily gauge measurements and remote sensing data to analyze the variability and seasonality of rainfall patterns over the Amazon Basin. We focused on the PERSIANN-CDR and CHIRPS precipitation datasets from 1983 to 2018 to quantify trends in predefined indices. The indices that were analyzed to assess variability of precipitation are NDD (Number of dry days); NXE (number of extreme events) during both wet and dry seasons; ORS (Onset of Rainy Season); and ERS (End of Rainy Season). We analyzed the trends for statistical significance and spatial similarity to identify hot spots of change. To connect pattern to process, we also simulated the land-atmosphere system using WRF to assess coupling strength and causality. We are running the simulation using CFSR 2010, with grid resolution of 16 km with the convection scheme active to capture small scale convective rainfall. Previous evidence has suggested an increasing trend on NDD during the dry season, a shift to a later onset and later cessation of the rainy season window, and an increasing trend in the NXE during both wet and dry seasons. The significance and spatial distribution of changes may vary over the region, but we anticipate that in the area with the largest percent of deforestation we will see the highest amount of changes in precipitation.