local CH4 anomaly. Regression-predicted values and residuals are presented in Figures 5 and 6, respectively. Figure 5 displays the expected percentile values of local CH4 concentration anomalies, given the extent of irrigated rice fields, during the months of field flooding. In Asia, the areas with highest expected values are in central and eastern China, western Korea (North and South), western Thailand, northern and southern Vietnam, northern and southern Philippines, Java (Indonesia), southern Myanmar, Sri Lanka, Bangladesh, northern and eastern India and central and southern Pakistan. Other regions with particularly-high expected local CH4 anomalies include the Iranian Caspian Sea littoral, the Russian Sea of Azov littoral, the Nile Delta, and California and the Mississippi River Valley in the United States.

# **5.2 The Role of Agricultural Techniques**

After controlling for the scale of flooded fields, we would also expect concentration anomalies to vary with the methane intensity of production techniques. Rice production in flooded fields produces methane because oxygen does not penetrate the soil when it is blocked by water. This promotes the growth of methane-producing bacteria. A variety of techniques can shorten the period of water blockage, including a single field drainage in the mid-season; alternate wetting and drying (AWD), dry seeding instead of transplanting rice into flooded fields, and “aerobic” systems that grow rice in well-drained soil (Adhya et al. 2014).

While perfect water management under ideal water supply conditions could theoretically reduce water blockage and methane emissions by 90%, actual adoption of methane-reducing techniques is strongly affected by the reliability of control over irrigation water, as well as potential variations in water coverage related to field size and degree of leveling (Adhya et al. 2014). Variation in these factors within and across regions can be expected to generate substantial differences in water blockage and methane intensity per hectare. Research on the effects of local production techniques remains limited to some areas (e.g. Nguyen et al. 2022; Devkota et al. 2019; Stuart et al. 2018). For rice production areas more generally, we would expect S5P CH4 observations to capture part of the interarea variation produced by different techniques.

# **5.3 Identifying Outlier Areas from Regression Residuals**

Figure 6 displays residuals from the regression results reported in Table 1. They have potential interest for policy analysis because they identify areas where variations in irrigation water use and field conditions may be associated with unusually high or low CH4 anomalies, after controlling for field scale. In Asia the areas with much higher-than-expected values are in northeastern China and Western India, while areas with much lower-than-expected values are in western China, southwestern China, Hainan (China), central Vietnam, southern Thailand, southern Philippines, Sri Lanka and the entirety of Indonesia. Elsewhere, areas with higher-thanexpected values include northern Italy, southern Nigeria, southeastern Brazil, central Bolivia and northern Colombia. Regions with notably lower-than-expected values include southeastern Australia, central Islamic Republic of Iran, the Nile Delta, northern Madagascar, southern Mali and eastern Guinea, northern Senegal, western Spain, Uruguay, western República Bolivariana de Venezuela, Costa Rica, Cuba, the Dominican Republic, western Mexico and California.

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