To investigate the regression relationships between greenhouse gas (GHG) emissions and soil microbes in a double-rice paddy soil under various management practices, a two-year study was conducted to observe the seasonal variation of GHG emissions and activities of soil microbes (SMA ) as well as their populations (SMP) using the closed static chamber-GC (gas chromatography) and the most probable number methods. There were seven management practices (or treatments), including CWS (Conventional Tillage + Without Straw Residues + Urea), NWS (No Tillage + Without Straw Residues + Urea), SCU (Conventional Tillage + Without Straw Residues + Controlled-Release Urea), HN (High Stubbles + No Tillage + Urea), HC (High Stubbles + Conventional Tillage + Urea), SN (Straw Cover + No Tillage + Urea) and SNF (Straw Cover + No Tillage + Urea + Continuous Flooding). The average values of seven treatments' daily fluxes of GHGs and SMA as well SMP were used for the analysis in this study. Regression analysis was conducted using the R statistical software. Similar seasonal variations of methane flux and SMA as well as the amount of soil methanogens (MET) were found in the rice growing season of 2008-2009; and same regularity occurred in the temporal distribution of nitrous oxide flux and the amount of soil nitrifiers and denitrifiers. Furthermore, there was a strong correlation between methane flux and SMA as well as the population of MET. The relationships of methane flux vs. SMA and methane flux vs. MET can be represented by using the exponential and quadratic polynomial models, respectively. Simple regression indexed that the quantity of MET could explain individually at least 96.96% of variance of methane flux (R²=0.969, P<0.001), but the fitting precision of multiple nonlinear regression of methane flux with two factors of SMA and MET (R²=0.975, P<0.001) was higher than the univariate regression analysis. Besides, the pronounced positive dependency of nitrous oxide flux with soil nitrifiers and denitrifiers has also been found (P<0.05). The mixed binary nonlinear regression of nitrous oxide flux with the SMP of the two types of microbes can explain at least 70.4% of variance of nitrous oxide flux (R²≥0.704, P<0.001), and of course the fitting precision of multiple nonlinear regression was higher than the simple regression using the SMP of either nitrifiers or denitrifiers. However, as we know, GHG emissions from paddy soils are affected by many factors, of which SMA and SMP are the most direct influential variants. In order to reasonably reveal the interactions between GHG emissions and environmental variables, the multivariate nonlinear regression analysis should be carried out based on data derived from the extensive field experiments rather than few laboratory trials.