Estimates of global riverine nitrous oxide (N2 O) emissions contain great uncertainty. We conducted a meta-analysis incorporating 169 observations from published literature to estimate global riverine N2 O emission rates and emission factors. Riverine N2 O flux was significantly correlated with NH4 , NO3 and DIN (NH4 + NO3 ) concentrations, loads and yields. The emission factors EF(a) (i.e., the ratio of N2 O emission rate and DIN load) and EF(b) (i.e., the ratio of N2 O and DIN concentrations) values were comparable and showed negative correlations with nitrogen concentration, load and yield and water discharge, but positive correlations with the dissolved organic carbon : DIN ratio. After individually evaluating 82 potential regression models based on EF(a) or EF(b) for global, temperate zone and subtropical zone datasets, a power function of DIN yield multiplied by watershed area was determined to provide the best fit between modeled and observed riverine N2 O emission rates (EF(a): R2 = 0.92 for both global and climatic zone models, n = 70; EF(b): R2 = 0.91 for global model and R2 = 0.90 for climatic zone models, n = 70). Using recent estimates of DIN loads for 6400 rivers, models estimated global riverine N2 O emission rates of 29.6-35.3 (mean = 32.2) Gg N2 O-N yr-1 and emission factors of 0.16-0.19% (mean = 0.17%). Global riverine N2 O emission rates are forecasted to increase by 35%, 25%, 18% and 3% in 2050 compared to the 2000s under the Millennium Ecosystem Assessment's Global Orchestration, Order from Strength, Technogarden, and Adapting Mosaic scenarios, respectively. Previous studies may overestimate global riverine N2 O emission rates (300-2100 Gg N2 O-N yr-1 ) because they ignore declining emission factor values with increasing nitrogen levels and channel size, as well as neglect differences in emission factors corresponding to different nitrogen forms. Riverine N2 O emission estimates will be further enhanced through refining emission factor estimates, extending measurements longitudinally along entire river networks and improving estimates of global riverine nitrogen loads.