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. Atlantic hurricane activity varies substantially from year to yearand so does the associated damage. Longer-term forecasting of hurricane risksis a key element to reduce damage and societal vulnerabilities by enablingtargeted disaster preparedness and risk reduction measures. While theimmediate synoptic drivers of tropical cyclone formation and intensificationare increasingly well understood, precursors of hurricane activity on longertime horizons are still not well established. Here we use a causal-network-based algorithm to identify physically interpretable late-springprecursors of seasonal Atlantic hurricane activity. Based on theseprecursors we construct statistical seasonal forecast models withcompetitive skill compared to operational forecasts. In particular, wepresent a skilful prediction model to forecast July to October tropicalcyclone activity at the beginning of April. Our approach highlights thepotential of applying causal effect network analysis to identify sources ofpredictability on seasonal timescales.

Robust predictors for seasonal Atlantic hurricane activity identified with causal effect networks