Seasonal forecasts are usually presented as three-month averages (like the one below). Why are seasonal forecasts not regularly provided on a monthly scale? Is this mostly related to predictability?
I understand that forecast skill in seasonal models mostly comes from persistent boundary conditions like SST anomalies (e.g. El Niño/La Niña). However, I would expect some boundary conditions to provide more predictability and more amplified anomalies in the first two months month rather than in the third or fourth month. For instance, the relatively shallow Baltic Sea could provide a high likelihood of warm conditions in the neighboring countries in the first two months when it is anomalously warm at the initialization time. However, its predictive value can be quickly lost when it cools down quickly after a cold episode. This in contrast to La Niña, which tends to be more persistent over time. To recap, I would expect monthly forecast anomalies to have some added value, because some climate predictors only provide skill at a monthly rather than at a seasonal scale.