Improved understanding of how our coasts will evolve over a range of time scales (years‐decades) is critical for effective and sustainable management of coastal infrastructure. A robust knowledge of the spatial, directional and temporal variability of the inshore wave climate is required to predict future coastal evolution and hence vulnerability. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980–2017) and show that 73% are directionally bimodal. We find that winter‐averaged expressions of six leading atmospheric indices are strongly correlated (r = 0.60–0.87) with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Regional inshore wave climate classification through hierarchical cluster analysis and stepwise multi‐linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (R2 = 0.45–0.8; p<0.05). We demonstrate for the first time the significant explanatory power of leading winter‐averaged atmospheric indices for directional wave climates, and show that leading seasonal forecasts of the NAO skillfully predict wave climate in some regions.

Plain Language Summary

Understanding the seasonal variability in wave climate around our coasts is fundamental for improving our understanding of how coasts will respond to climate change and sea‐level rise. Recent research has highlighted the importance of wave direction on coastal response. In this study we specifically explore the seasonal variability in wave direction throughout the inshore regions of the United Kingdom and Ireland at 63 locations between 1980 and 2017. We find that 73% of sites examined are directionally bimodal. We also find that combinations of six of the regions leading climate indices (NAO, AO, WEPA, EA, SCAND, EA/WR) are strongly correlated with both total and directional winter wave power at all the studied sites. We show that regression models using combinations of these climate indices have significant skill in predicting directional wave climates over the period 1980‐2017. For the first time we show that 'seasonal ahead' forecasts of the NAO can skilfully predict wave climate in some regions of the United Kingdom and Ireland, which could be used as tool to support coastal hazard mitigation.