  Agricultural food production and natural ecological systems depend on a range
of seasonal climate indicators that describe seasonal patterns in
climatological conditions. This paper proposes a probabilistic forecasting
framework for predicting the end of the freeze-free season, or the time to a
mean daily near-surface air temperature below 0 $^\circ$C (here referred to as
hard freeze). The forecasting framework is based on the multi-model seasonal
forecast ensemble provided by the Copernicus Climate Data Store and uses
techniques from survival analysis for time-to-event data. The original mean
daily temperature forecasts are statistically post-processed with a mean and
variance correction of each model system before the time-to-event forecast is
constructed. In a case study for a region in Fennoscandia covering Norway for
the period 1993-2020, the proposed forecasts are found to outperform a
climatology forecast from an observation-based data product at locations where
the average predicted time to hard freeze is less than 40 days after the
initialization date of the forecast on October 1.
