Nitrous oxide (N2O) has a high global warming potential and depletes stratospheric ozone. The U. S. Corn Belt plays an important role in the global anthropogenic N2O budget. To date, studies on local surface N2O emissions and the atmospheric N2O budget have commonly used Lagrangian models. In the present study, we used an Eulerian model – Weather Research and Forecasting Chemistry (WRF-Chem) model to investigate the relationships between N2O emissions in the Corn Belt and observed atmospheric N2O mixing ratios. We derived a simple equation to relate the emission strengths to atmospheric N2O mixing ratios, and used the derived equation and hourly atmospheric N2O measurements at the KCMP tall tower in Minnesota to constrain agricultural N2O emissions. The modeled spatial patterns of atmospheric N2O were evaluated against discrete observations at multiple tall towers in the NOAA flask network. After optimization of the surface flux, the model reproduced reasonably well the hourly N2O mixing ratios monitored at the KCMP tower. Agricultural N2O emissions in the EDGAR42 database needed to be scaled up by 19.0 to 28.1 fold to represent the true emissions in the Corn Belt for June 1–20, 2010 – a peak emission period. Optimized mean N2O emissions were 3.00–4.38, 1.52–2.08, 0.61–0.81 and 0.56–0.75 nmol m− 2 s− 1 for June 1–20, August 1–20, October 1–20 and December 1–20, 2010, respectively. The simulated spatial patterns of atmospheric N2O mixing ratios after optimization were in good agreement with the NOAA discrete observations during the strong emission peak in June. Such spatial patterns suggest that the underestimate of emissions using IPCC (Inter-governmental Panel on Climate Change) inventory methodology is not dependent on tower measurement location.