El Niño Southern Oscillation (ENSO) has been linked to climate and hydrologic anomalies throughout the world. This paperpresents how ENSO modulates the basic statistical characteristics of streamflow time series that is assumed to be affected byENSO. For this we first considered hypothetical series that can be obtained from the original series at each station by assumingnon-occurrence of El Niño events in the past. Instead those data belonging to El Niño years were simulated by the RadialBased Artificial Neural Network (RBANN) method. Then we compared these data to the original series to see a significant differencewith respect to their basic statistical characteristics (i.e., variance, mean and autocorrelation parameters). Various statisticalhypothesis testing methods were used for four different scenarios. Consequently if there exist a significant difference,then it can be inferred that the ENSO events modulate the major statistical characteristics of streamflow series concerned. Theresults of this research were in good agreement with those of the previous studies.