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. Probabilistic seasonal forecasts are important for manywater-intensive activities requiring long-term planning. Among the differenttechniques used for seasonal forecasting, the ensemble streamflow prediction(ESP) approach has long been employed due to the singular dependence on pastmeteorological records. The Swedish Meteorological and HydrologicalInstitute is currently extending the use of long-range forecasts within itsoperational warning service, which requires a thorough analysis of thesuitability and applicability of different methods with the national S-HYPEhydrological model. To this end, we aim to evaluate the skill of ESPforecasts over 39 493 catchments in Sweden, understand their spatio-temporalpatterns, and explore the main hydrological processes driving forecastskill. We found that ESP forecasts are generally skilful for most of thecountry up to 3 months into the future but that large spatio-temporalvariations exist. Forecasts are most skilful during the winter months innorthern Sweden, except for the highly regulated hydropower-producingrivers. The relationships between forecast skill and 15 differenthydrological signatures show that forecasts are most skilful forslow-reacting, baseflow-dominated catchments and least skilful for flashycatchments. Finally, we show that forecast skill patterns can be spatiallyclustered in seven unique regions with similar hydrological behaviour. Overall,these results contribute to identifying in which areas and seasons and how longinto the future ESP hydrological forecasts provide an added value, not onlyfor the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management andrisk reduction.

Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden