Accurate wind speed forecasting is essential for optimizing renewable energy production and supporting climate resilience initiatives. This study explores the application of machine learning, specifically Long Short-Term Memory (LSTM) networks, for downscaling wind speed data from coarse 100 km \(^2\) grid resolution to a finer 10 km \(^2\) grid. Using a dataset spanning 30 years from Portugal and a small part of Spain, the new model is based on historical information on both coarse- and fine-resolution wind speed data to improve the quality of the predictions. A one-for-all model was developed using randomly selected, equal-sized samples from each location and day of the year, drawn from the available data. The model training and validation were conducted via 5-fold cross-validation. The model including both fine and coarse information showed the best results, achieving an RMSE of 0.52 ± 0.01 and \(\text {R}^{2}\) of 0.53 ± 0.01 over validation. Furthermore, the spatial distribution of predicted wind speeds is closely aligned with observed patterns, further confirming the ability of the model to capture adequately the spatial variations of wind speed. Additionally, residuals from the model exhibited no statistically significant autocorrelation, indicating that the model has successfully captured the underlying temporal evolution of the time series data. These findings highlight the importance of combining coarse- and fine-resolution data to enhance wind speed predictions, with implications for renewable energy production and climate studies.