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Conceptual rainfall‐runoff models account for the spatial dynamics of hydrological processes in a basin using simple spatially lumped storage‐flow relations. Such rough approximations introduce model errors that are often difficult to characterize. Here, we develop and apply a methodology that recursively estimates and accounts for model errors in real‐time streamflow prediction settings by adding time‐dependent random noise to the internal states (storages) of the hydrological model. Magnitude of the added noise depends on a precision (inverse variance) parameter that is estimated from rainfall‐runoff data. A recursive Bayesian technique is used for estimation: posteriors of hydrological parameters and states are updated through time with an ensemble Kalman filter, whereas the posterior of the precision parameter is updated recursively using a novel gamma density approximation technique. Applying this algorithm to different model error scenarios allows identification of the main source of model errors. The methodology is applied to short‐term streamflow prediction with the Hymod rainfall‐runoff model in a semi‐cold, semi‐humid basin in Iran. Results show that (i) streamflow prediction in this snow‐dominated basin is more affected by model errors in the slow flow than the quick flow component of the model, (ii) accounting for model errors in the slow flow component improves both low and high flow predictions, and (iii) predictive performance further improves by accounting for Hymod parameter uncertainty in addition to model errors. Overall, accounting for model errors increased Nash‐Sutcliffe efficiency (by 1–5%), reduced mean absolute error (by 2–43%), and improved probabilistic predictive performance (by 50–80%).

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One of the most important stages in climate-change-impact studies is uncertainty analysis, due to its great effect on both predictions and decision-making. This study presents a procedure that characterizes the changes of climatic variables for the period 2011-2040 under representative concentration pathway (RCP) scenarios and then quantifies the uncertainty linked with the downscaling process using a bootstrapping method at 95% confidence intervals in one of the most vulnerable basins, the "Karaj-Jajrud" located in the South Alborz Range, Iran. The results show that there is a consistent warming in mean air temperature time-series with different magnitudes for all the RCP scenarios in the region for 2011-2040, whereas the results indicate decreasing precipitation compared with the baseline period for all RCP scenarios in the study area. Analysing the impacts of the downscaling process uncertainty on the prediction results shows that the contribution of this uncertainty source to the prediction uncertainty is relatively high, as about 30% of the downscaled temperature and precipitation data fall inside the 95% simulation confidence intervals. Furthermore, precipitation-series uncertainty is more than the air temperature series. Climate change assessments and their uncertainty analysis can help managers to enhance preparedness and adaptation strategies in order to mitigate the consequences of natural hazards. More investigations can be done by adopting more general circulation models and other downscaling methods to compare the uncertainty that arises from each uncertainty source.