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A Monte Carlo analysis was combined with an empirically based approach to quantify uncertainties in soil nitrous oxide (N₂O) emissions from U.S. croplands estimated with the DAYCENT simulation model. Only a subset of croplands was simulated in the Monte Carlo analysis, which was used to infer uncertainties across the larger spatiotemporal domain. Specifically, one simulation representing dominant weather, soil type, and N inputs was performed for each major commodity crop in the 3000 counties occurring within the conterminous United States. We randomly selected 300 counties for the Monte Carlo analysis and randomly drew model inputs from probability distribution functions (100 iterations). A structural uncertainty estimator was developed by deriving a statistical equation from a comparison of DAYCENT‐simulated N₂O emissions with measured emissions from experiments in North America. We …