© 2016 John Wiley & Sons Ltd Estimates of global riverine nitrous oxide (N2O) emissions contain great uncertainty. We conducted a meta-analysis incorporating 169 observations from published literature to estimate global riverine N2O emission rates and emission factors. Riverine N2O flux was significantly correlated with NH4, NO3and DIN (NH4 + NO3) concentrations, loads and yields. The emission factors EF(a) (i.e., the ratio of N2O emission rate and DIN load) and EF(b) (i.e., the ratio of N2O 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 N2O 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 N2O emission rates of 29.6–35.3 (mean = 32.2) Gg N2O–N yr−1and emission factors of 0.16–0.19% (mean = 0.17%). Global riverine N2O 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 N2O emission rates (300–2100 Gg N2O–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 N2O 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.