<p>As industries like agriculture and energy become increasingly reliant on weather information, there is a growing need for accurate medium to long-term precipitation forecasts. This study examined the skill of the Regional Climate Model v4.7 (RegCM4), forced by the CFSv2, in forecasting intra-seasonal to seasonal precipitation over seven specific basins in Iran. The analysis covered the period 2000–2019 and considered lead times of one to three months (L1 to L3). Deterministic and categorical statistical metrics were used to evaluate the model’s skill. Results indicate significant regional variability in model performance, suggesting that climatic factors influence the model’s accuracy. Deterministic metrics reveal that the RegCM4-CFSv2 model is better at forecasting precipitation for basins along the southern Caspian Sea coastal plains than for basins in central Iran. While categorical metrics exhibited the highest proficiency in forecasting the correct precipitation category for basins in the coastal plains of the Persian Gulf and the lowest proficiency for basins in northwestern Iran. Overall, the model exhibits reasonable skill, particularly at shorter lead times (e.g. L1). At the L1, the average Kling-Gupta efficiency (KGE) and correlation coefficient (CC) are around 0.3, and the relative root mean square error (RMSE) is approximately 28%. Performance weakens with increasing lead time, with significant deterioration at 3-month lead time (L3). Categorical metrics like probability of detection (POD) and threat score (TS) indicate moderate accuracy, especially for ‘below-normal (BN)’ and ‘above-normal (AN)’ categories. However, the model is prone to false alarms, particularly in the ‘normal (N)’ precipitation category. Results highlight the model’s ability to detect upper and lower tercile categories (33rd and 66th percentiles), making it a valuable tool for developing effective flood or drought management strategies. Overall, the model shows promise for monthly precipitation forecasts but has limitations for long-range (beyond one month) prediction.</p> Graphical abstract <p>The graphical abstract in this study presents the application of the regional climate model (RegCM-4.7), forced by CFSv2, for intra-seasonal to seasonal forecasting in seven hydrological regions across Iran. An ensemble approach with 24 members, derived from multiple initial conditions, model parameters, and physics options, was employed in the design of the modeling system. These simulations span the years 2000–2019, focusing on the October-April period, and considered lead times of one to three months ahead. Deterministic and categorical statistical metrics were used to evaluate the model’s skill in different regions and lead-times. Observational data from 157 synoptic stations in Iran served as the basis for comparing model results. The model’s performance showed a regional variability, indicating that forecast accuracy varies not only regionally but also with lead time. The study highlights how dynamical downscaling with a regional climate model can effectively create intra-seasonal to seasonal precipitation forecast for water resources management. Seasonal precipitation forecasts are more accurate after dynamic downscaling of global model output.</p> <p></p>