Dear Linnéa, Maria and Ronald,
I will quickly add a few comments to what Ronald already wrote.
Maria, thank you very much for this feedback. It is extremely valuable to hear about local model performance. I am looking forward to hearing your feedback for wetter months, once the time comes. As for temperature and precipitation, note that a bias correction based on the GFD dataset has now been implemented (as of August 25th). Hopefully bias corrected forecasts will better fit local observations. I really like the suggestion of adding other evaluation metrics, and it could be explored in the future. It does raise the challenge of graphically communicating large amounts of data while keeping clear the purpose of each metric, and to overcome this, user feedbacks would be key.
Linnéa, I also really like the idea to link seasonal forecast skill with wetter/colder/drier conditions. For now, the skill graphs in the interface provide this information for each month of the year. The month of the year can be seen as a proxy to these changing conditions, if you consider the entire year. For a more detailed evaluation, of, for example, dry months of January, longer datasets would be necessary to obtain statistically robust evaluations.