Résumé
Irrigation water management in the Mediterranean region faces major challenges due to climate variability changes. To address this reality, tools that support early decision-making are increasingly needed. While seasonal forecasts (SWF) have demonstrated potential to assist the agricultural sector, their practical application in irrigation management remains limited. This study evaluates the accuracy and skill of raw (non-bias-corrected) SWF in predicting weather and climatic demand conditions, crop cycle duration estimated using the growing degree day (GDD) approach and crop irrigation water requirements across the Mediterranean region, focusing on how seasonality and location factors may influence their usefulness. The Lucefecit Collective Irrigation Scheme (Portugal), the Crau irrigated area (France), and the Thessaly Plain (Greece) were selected as key irrigation locations in the Mediterranean region. Durum wheat and maize were selected as representative autumn-winter (AW) and spring-summer (SS) crops, respectively. SWF data were generated within the HubIS project (PRIMA/ 0006/2019) using the Weather Research and Forecasting system. The results showed that SWF performance varied by predicted variable, season, and location. Overall, the forecasts were more accurate in predicting matic demand (ETo) than precipitation, with mean absolute errors (MAE) of 17.0-170.3 mm and 94.8-438.6 mm, respectively. Higher irrigation demands were predicted, especially in SS, as SWF tended to overestimate ETo to 20 %), underestimate seasonal accumulated precipitation (up to 90 %), underestimate temperature (up to degrees C) and extend crop cycles (up to 60 days). Irrigation requirements anomaly signals were better captured wheat than for maize. SWF exhibited less skill than the long-term (10-years) weather observed average in almost all cases, except for AW ETo predictions in Portugal. Nevertheless, the range of irrigation requirements (for wheat 0-489 mm and for maize 563-960 mm) suggests SWF hold potential for early irrigation water management, which could be better achieved with appropriate forecast data corrections.