The seasonal climate forecast for the region of Mount Everest shows the monthly mean temperature and precipitation anomalies for the next 6 months in the top panel. The forecast is regional for areas of 100 km by 100 km or larger.
The seasonal forecast provides climate characteristics such as mean values or anomalies for an entire month. Anomalies are deviations from the climatological mean. Thus, a negative temperature and precipitation anomaly indicates cooler and drier than average conditions. Climatological information allow little inference on the expected weather. Assume a month with a positive anomaly of +1 degree. It is very unlikely that every hour of this month is 1 degree warmer. A more realistic scenario is that some days are significantly warmer than average, while others are on average. Most importantly, there might also be some days that are colder or even significantly colder than average, so the positive anomaly is not at all a guarantee to have e.g. no frost.
A seasonal weather forecast for particular day is not technically possible: it is statistically more unreliable than a climatic average. The reason is that daily weather is subject to larger swings influenced by mesoscale or microscale events, and originating factors cannot be measured precisely enough, so daily weather forecasts become statistically more unreliable than a climatic average about 10-14 days ahead. You probably noticed the unreliability of a 10-day weather forecast and predicting several months is clearly more difficult.
As seasonal forecasts can be more or less reliable, we provide the results of several hundred forecasts to better estimate a trend. We combine all the seasonal forecasts computed by the major Centers and Institutions worldwide into a Super-Ensemble (ENSEMBLE) that is more likely to be correct than a forecast from a single Institution. If you see that forecasts of different models contradict each other, then there is very little hope of forecasting the season for that time period. There are some regions and situations where seasonal forecasts can be quite accurate. The most well-known examples are El Niño and La Niña situations.
The different models presented here are computed by: the European Center of Medium Range Weather Forecast (ECMWF), the National Center of Environmental Prediction (NCEP/NOAA), the German Weather Service (DWD), the UK-MetOffice (UKMO), MeteoFrance (METEOFR), the Japan Meteorological Agency (JMA) and the Euro-Mediterranean Center on Climate Change (CMCC). The Agencies/Centers update their forecasts about once per month, but not all do so at the same time. We therefore indicate the forecast-run of each center in the diagram. We recompute the ensemble whenever one of the centers updates a forecast.