Interannual to decadal seasonal streamflow variability has major implications for flood risk, drought management, water resources and hydropower production across Great Britain. A substantial component of this variability is linked to large-scale atmospheric circulation, particularly over the North Atlantic, but its contribution to seasonal streamflow variability remains insufficiently quantified. Here, we use a dynamical adjustment framework based on multiple linear regression to quantify the contribution of synoptic-scale weather regimes to seasonal Standardised Streamflow Index (SSI) variability across Great Britain. The method is applied to reconstructed SSI series for 167 non-overlapping catchments, which are aggregated to the UKCP18 river regions.</p> <p>The strongest dynamical contribution occurs in winter, especially across western and northern Great Britain, where weather-regime-driven SSI explains around 70% of interannual winter SSI variance in several regions. The dynamically driven contribution to SSI also explains a substantial fraction of spring and autumn variability, but the relationship is weaker in summer, consistent with the reduced influence of synoptic-scale circulation on warm-season rainfall. In one third of GB regions, including the Clyde, Solway, and North-West England, dynamically driven SSI captures much of the observed interannual and decadal variability, including several historically wet and dry winters. This strong circulation–streamflow relationship provides useful information for seasonal forecasting, climate-model evaluations and historical reconstruction.</p> <p>Applying the same framework to weather-regime sequences from the UKCP18 global climate models ensemble shows that the ensemble systematically under-represents interannual variance in winter dynamically driven SSI, with an ensemble-mean bias of approximately -20% relative to observations and substantial spatial and inter-member variation. These results show that atmospheric circulation biases can propagate into climate-model-driven assessments of seasonal streamflow variability, with implications for hydrological projections, model evaluation, and process-informed bias correction.