The Added Value of Statistical Seasonal Forecasts.

Seasonal climate predictions can assist with timely preparations for extreme episodes, such
as dry or wet periods that have associated additional risks of droughts, fires and challenges for water
management. Timely warnings for extreme warm summers or cold winters can aid in preparing
for increased energy demand. We analyse seasonal forecasts produced by three different methods:
(1) a multi-linear statistical forecasting system based on observations only; (2) a non-linear random
forest model based on observations only; and (3) process-based dynamical forecast models. The
statistical model is an empirical system based on multiple linear regression that is extended to include
the trend over the previous 3 months in the predictors, and overfitting is further reduced by using
an intermediate multiple linear regression model. This results in a significantly improved El Niño
forecast skill, specifically in spring. Also, the Indian Ocean dipole (IOD) index forecast skill shows
improvements, specifically in the summer and autumn months. A hybrid multi-model ensemble is
constructed by combining the three forecasting methods. The different methods are used to produce
seasonal forecasts (three-month means) for near-surface air temperature and monthly accumulated
precipitation seasonal forecast with a lead time of one month. We find numerous regions with added
value compared with multi-model ensembles based on dynamical models only. For instance, for
June, July and August temperatures, added value is observed in extensive parts of both Northern
and Southern America, as well as Europe.