The Amazon Basin plays an important role in the global climate and carbon budget, with natural emissions and removals of CO₂ and CH₄prevailing in the wet season. Accurate modeling of the transport of greenhouse gases (GHG) is essential for understanding the contributions of sources, sinks, and atmospheric processes. High-resolution atmospheric models offer a compromise between computational cost and physical realism, capturing mesoscale processes that influence GHG transport in the Amazon. We evaluate the WRF-GHG model (Weather Research and Forecasting Model with GHG module) using CO₂ and CH₄ measurements from the Amazon Tall Tower Observatory (ATTO) and aircraft observations during the CAFE-Brazil campaign in January 2023. Simulations employed two domains centered at ATTO, with MapBiomas land cover data, region-specific parameters for CO₂ biogenic emissions and removals, and multiple wetland CH₄ emission configurations. Comparisons with ATTO indicate that the regionally adapted biogenic flux parameterizations improved the representation of CO₂ and net ecosystem exchange (NEE). For CH₄, CAMS Inversion-optimized flux product best reproduces observed concentrations, while the Kaplan diagnostic model and WetCHARTs inventories overestimate near-surface mole fractions. The model predicts diurnal CH₄ fluctuations, controlled by boundary-layer dynamics and atmospheric transport, that were not observed at ATTO. Comparisons with CAFE-Brazil aircraft data indicated regional wetland sources and wind-driven transport driving the observed CH₄ enhancements. These findings underscore the importance of improving the parameterization of biogenic fluxes to enhance the capability of models in complex tropical environments like the Amazon Basin and to accurately describe the complex circulation between rivers, upland areas, and floodplains.