Despite the recent effort to develop underpinning climate prediction science for seasonal to decadal (S2D) climate predictions, there has been relatively little uptake and use of S2D climate forecasts by users for decision making in Europe [1]. On the other hand, there is a much longer tradition in applying seasonal climate forecast information for user applications in other parts of the World, notably in Africa, the USA and Australia [1,2]; "one notable exception" is the use of precipitation forecasts for hydropower generation management by Electricte de France (EDF Energy) [3,4]. In part, this is related to the relatively limited skill of S2D climate forecasts in Europe; in contrast predictability in decadal hindcasts (forecasts of the past) is greatest in the Tropics [5]. This illustrates the importance of understanding skill in user uptake of such products [6][7][8]. However, accuracy, lead time, and appropriate spatial and temporal scale of S2D climate forecast information may not be the main (or only) factors influencing user uptake; potential economic and environmental benefits may be of greater importance [9]. In addition, probabilistic (ensemble) prediction systems are more commonly used in medium-range applications, bringing additional challenges in communicating forecast information to end-users.The use of basic S2D climate forecast outputs (e.g. temperature, precipitation etc) has significant potential to support both shorterterm decision making (thus helping avoid potential risks and losses, and optimize profits), and longer-term climate adaptation plans in numerous sectors (e.g. agriculture, water, health and energy [10]) and as noted above these products are already widely used in some regions of the World. Further benefit could also be realized by providing information more directly relevant to potential users, such as changes in extreme rainfall events, heat-waves, crop yields and river flows, which we refer to here as "impacts". In addition, further processing of direct S2D forecast outputs and the use of impact models may improve the usability of S2D forecasts with weak skill [3,4]. However, as noted above, the skill of S2D forecasts for impacts (as opposed to generic assessments of weather and climate skill) may limit the usability of S2D impacts products. S2D predictions of weather and climate can be derived both from statistical (or empirical) and dynamic models [7]. The former approach is usually based on regional historic relationships between climate variables; most recent dynamic approaches use fully coupled ocean-atmosphere general circulation models (CGCMs). Some S2D forecasting systems, particularly the CGCM approaches, may include impact-relevant outputs directly ("online" approaches), for example via river flow models, soil moisture calculations, or estimates of vegetation productivity. Validation and skill assessment in these systems may also provide valuable information on the overall performance of the seasonal prediction system itself. For example, rivers integrate land hydrolo...