Many decisions in the field of agriculture, forestry and/or hydrology can get profit from seasonal forecast. However, the skill of such forecast is a critical issue to promote their use in operational context and get profitable decisions. If many methods to assess meteorological forecast performances are available, they are mostly implemented on raw climate variables, while their implementation in sectorial application remains limited to some case studies. In this study a wide range of indicators covering most of the decision-making needs in agriculture, forestry and in some extent to hydrology were considered. These indicators are either direct climate variables, a combination of climate variables, or variables calculated by dynamic models (e.g. a crop model). The study was implemented in southern France using the M&#233;teo-France system 6 1993-2016 hindcast, downscaled using the UERRA reanalysis and the ADAMONT methods available in CS-Tools R package developed in the frame of the MEDSCOPE project. These computed indicators need various climate variables as wind speed, radiation and air humidity while most of the downscaling methods were designed for air temperature and precipitation. The main results are the following.</p><ul><li>We showed that all variables led to comparable level of accuracy. Seasonal forecasts provide added value compared to climatological forecasts with Brier Skill Scores between 0.05 and 0.20.</li> <li>The predictability of the number of rainy days or the number of days with temperature above a threshold is comparable to those of the corresponding scalar quantities such as cumulative precipitation or mean air temperature. However seasonal forecast of extreme events such as heat waves or drought episodes was not possible.</li> <li>Indicators combining several climatic information such as potential evapotranspiration or fire weather index have comparable predictability than the individual climate variables used in the calculation.</li> <li>With indicators based on dynamic models, the memory effect, i.e. the effect of the system state at the beginning of the forecast period, has a strong impact on the skill scores. We propose a methodology based on an ANOVA to qualify this memory effect by using the F-value. It is shown that when the memory effect is strong (F-value >10) the seasonal forecast does not bring any added value compared to the climatological forecast.;An evaluation of the interest of a seasonal forecast in a decision-making framework was carried out by an economic approach. We have based our analysis on the decision making based on the forecast of an event. We show that there is a generic relationship between the AUC score and the gain from the forecast. We show that this relationship depends on the frequency of the decision event, the rarer the event the higher the AUC value must be to have a profitable decision. In our case, a decision based on the detection of a tercile leads to a profitable decision in more than half of the indicators while no indicator leads to a profitable decision when it is based on the detection of a quintile.