Effectively quantifying hot moments of nitrous oxide (N2O) emissions from agricultural soils is critical for managing this potent greenhouse gas. However, we are challenged by a lack of standard approaches for identifying hot moments, including (1) determining thresholds above which emissions are considered hot moments, and (2) considering seasonal variation in the magnitude and frequency distribution of net N2O fluxes. We used one year of hourly N2O flux measurements from 16 autochambers that varied in flux magnitude and frequency distribution in a conventionally tilled maize field in central Illinois, USA to compare three approaches to identify hot moment thresholds: 4x the standard deviation (SD) above the mean, 1.5x the interquartile range (IQR), and isolation forest (IF) identification of anomalous values. We also compared these approaches on seasonally subdivided data (early, late, non-growing seasons) vs. the whole year. Our analyses of the datasets revealed that 1.5x IQR method best identified N2O hot moments. In contrast, the 4 SD method yielded hot moment threshold values too high, and the IF method yielded threshold values too low, leading to missed N2O hot moments or low net N2O fluxes mischaracterized as hot moments, respectively. Furthermore, seasonally subdividing the dataset facilitated identification of smaller hot moments in the late and non-growing seasons when N2O hot moments were generally smaller, but it also increased hot moment threshold values in the early growing season when N2O hot moments were larger. Consequently, we recommend using the 1.5x IQR method on whole year datasets to identify N2O hot moments.