Accumulating evidence indicates that N2O emission factors (EFs) vary with nitrogen additions and environmental variations. Yet the impact of the latter was often ignored by previous EF determinations. We developed piecewise statistical models (PMs) to explain how the N2O EFs in agricultural soils depend upon various predictors such as climate, soil attributes, and agricultural management. The PMs are derived from a new Bayesian Recursive Regression Tree algorithm. The PMs were applied to the case of EFs from agricultural soils in China, a country where large EF spatial gradients prevail. The results indicate substantial improvements of the PMs compared with other EF determinations. First, PMs are able to reproduce a larger fraction of the variability of observed EFs for upland grain crops (84%, n=381) and paddy rice (91%, n=161) as well as the ratio of EFs to nitrogen application rates (73%, n=96). The superior predictive accuracy of PMs is further confirmed by evaluating their predictions against independent EF measurements (n=285) from outside China. Results show that the PMs calibrated using Chinese data can explain 75% of the variance. Hence the PMs could be reliable for upscaling of N2O EFs and fluxes for regions that have a phase-space of predictors similar to China. Results from the validated models also suggest that climatic factors regulate the heterogeneity of EFs in China, explaining 69% and 85% of their variations for upland grain crops and paddy rice, respectively. The corresponding N2O EFs in 2008 are 0.84±0.18% (as N2O-N emissions divided by the total N input) for upland grain crops and 0.65±0.14% for paddy rice, the latter being twice as large as the IPCC Tier 1 defaults. Based upon these new estimates of EFs, we infer that only 22% of current arable land could achieve a potential reduction of N2O emission of 50%.