In this study we explore the seasonal predictability of water resources in a Mediterranean environment (the Boadella reservoir, in north-eastern Spain). Its utility for end-users is assessed through the analysis of economic value curve areas (EVA). Firstly, we have built monthly multiple linear regression (MLR) models for the in-flow, out-flow and volume anomalies by identifying the underlying relationships between these predictands and their potential predictors, both meteorological and human influenced: rainfall, maximum and minimum temperatures, reservoir volume and discharge. Subsequently, we have forecast the monthly anomalies with these models for the period 1981–2010 (up to seven months ahead). We have tested the aforementioned models with four strategies in a leave-one-out cross-validation procedure (LOOCV): a) Climatology (Clim.), b) persistence (Pers.), c) antecedent observations + climatology (A + Clim.), d) antecedent observations + European Centre for Medium-range Weather Forecasts (ECMWF) System 4 anomalies (A + S4). Climatology is the operational strategy against which the other approximations are compared. The second and third approaches only use observations as input data. Finally, the last one combines both observations and ECMWF System 4 forecasts. The LOOCV revealed that reservoir volume is the variable best described by the MLR models, followed by in-flow and out-flow anomalies. In the case of volume anomalies, the predictability displayed provides added value with respect to climatology with a minimum of four months in advance. For in-flow and out-flow this is true at one month ahead, and regarding the latter variable we encounter enhanced predictability also at longer horizons for the summer months, when water demands peak (a valuable result for end-users). Hence, there is a window of opportunity to develop future operational frameworks that could outperform the use of climatology for these variables and forecast horizons.