I’d like to respond to two issues you raise:
– the skill of predicting snow melt in spring summer. As I also explained in previous post (just above this one), the models do this fairly well primarily because we know the amount of snow present in a basin at the start of a forecast also fairly well. So if you mean by ‘linking forecast’s to present large scale weather’ linking it to present large scale snow distribution, then that is exactly what the forecast models do!
– however if you mean linking it to the present atmospheric state, that is not something we do (yet), but that might be a good idea to explore and do in the future. Then we would be issueing so-called conditional forecasts. For example, if in December we are in a specific strong positive or negative state of the North Atlantic Oscillation (NAO, an important predictor of European weather), then we might have more confidence in our forecasts than if the NAO is weakly developed.
To some extend this is done implicitly in our model-chain as our maps show high, medium, more low probabilities of e.g. Above Normal rain or river flow. A high probability means we are more confident about the forecast. However, at this stage we cannot tell you why (in causal terms) it is high, we only know then that the majority of the model runs predict this. More research (and operational development) is needed before we can tell you for each forecast why high or low river flows are predicted.