Riverine ecosystems are a significant source of nitrous oxide (N₂O) worldwide, but how they respond to human and natural changes remains unknown. In this study, we developed a compound model chain that integrates mechanism-based modeling and machine learning to understand N₂O transfer patterns within land, rivers, and the atmosphere. The findings reveal a decrease in N₂O emissions in the Yangtze River basin from 4.7 Gg yr⁻¹ in 2000 to 2.8 Gg yr⁻¹ in 2019, with riverine emissions accounting for 0.28% of anthropogenic nitrogen discharges from land. This unexpected reduction is primarily attributed to improved water quality from human-driven nitrogen control, while natural factors contributed to a 0.23 Gg yr⁻¹ increase. Notably, urban rivers exhibited a more rapid N₂O efflux ($F_{{\mathrm{N}}_2\mathrm{O}}$), with upstream levels nearly 3.1 times higher than rural areas. We also observed nonlinear increases in $F_{{\mathrm{N}}_2\mathrm{O}}$ with nitrogen discharge intensity, with urban areas showing a gradual and broader range of increase compared to rural areas, which exhibited a sharper but narrower increase. These nonlinearities imply that nitrogen control measures in urban areas lead to stable reductions in N₂O emissions, while rural areas require innovative nitrogen source management solutions for greater benefits. Our assessment offers fresh insights into interpreting riverine N₂O emissions and the potential for driving regionally differentiated emission reductions.