Water resource forecasting generally centres on understanding hydrological variability over coming months or years, so that water managers can prepare for extremes such as droughts or floods (Chang & Guo, 2020; Hao et al., 2018). Some forecasting systems seek to project further into the future to allow long-term planning of infrastructure and resilience to extremes and climate change (Svensson et al., 2015). These systems can rely directly or indirectly on outputs from Global Climate Models (GCMs; such as gridded reanalysis datasets) to forecast hydrological conditions (Bhatt & Mall, 2015; Ionita & Nagavciuc, 2020). In the North Atlantic region, in particular Western Europe, the North Atlantic Oscillation (NAO) is used as an indicator for hydrometeorological conditions given its leading control on winter rainfall totals (Hurrell & Deser, 2010; Scaife et al., 2008, 2014). A dipole of pressure anomalies over the North Atlantic, the NAO's positive phase (greater than average pressure gradient; NAO+) results in wetter conditions in northwest Europe with dryer conditions in southwest Europe (Rust et al., 2018; Trigo et al., 2004). Its negative phase (weaker than average pressure gradient; NAO−) results in the inverse effect on rainfall (Folland et al., 2015; and as shown by the correlation coefficients in Figure 1). Given this relationship, and, con