Nitrous oxide (N₂O) emissions from agricultural soils contribute 4% of total anthropogenic greenhouse gas (GHG) emissions globally. Events known as ‘hot moments’ can occur following environmental changes that favor N₂O production, which contribute disproportionately to annual cumulative emissions. Despite their significance, hot moments and their impact have not been statistically well defined, particularly on a global scale. We collected 13,787 soil N₂O flux measurements from 42 publications and evaluated 14 methods of statistical anomaly detection for their ability to identify hot moments within datasets. Two methods achieved highest overall performance by Matthews correlation coefficient (MCC): median absolute deviation (MCC: 0.80) and minimum covariance determinant (MCC: 0.80), the latter which also performed evenly across highly dissimilar datasets and identified more contextually important minor hot moments (39%) that other methodologies may misidentify. Interquartile range, which has previously been used and recommended, performed poorly when hot moments were either very rare or very common within a dataset, and identified few local hot moments (14%). Overall, hot moments comprised 19% of measurements while contributing 75% of cumulative emissions. The median background N₂O emission reported in all datasets was 2.2 g N ha ^-1 day ^-1, while the median hot moment emission was 10-fold higher, ranging from 23 to 25 g N h ^-1 day ^-1. These findings advance knowledge of how to accurately define and identify hot moments globally - a crucial task to investigating and mitigating these critical biogeochemical events.