This paper dynamically combines three independent forecasts of multiple river flow volumes a season in advance for arid catchments. The case study considers five inflow locations in the upper Namoi Catchment of eastern Australia. The seasonal flows are predicted on the basis of concurrent sea surface temperature anomalies (SSTAs), which are predicted a season forward using a dynamic combination of three SSTA forecasts. The river flows are predicted using three statistical forecasting models: (1) a mixture of generalized lognormal and multinomial logit models, (2) the local regression of independent components of five inflows, and (3) the weighted nearest neighbor method, where each of these models use the forecasted SSTA along with prior lags of the flow as the main driving variables. The study demonstrates that improved SSTA forecast (due to dynamic combination) in turn improves all three flow forecasts, while the dynamic combination of the three flow forecasts results in further, although smaller, improvements.