Predicting streamflow in ungauged basins (PUB) remains a central challenge in hydrology. Long short-term memory (LSTM) networks trained on large samples of catchments ("global" LSTMs) have emerged as a state-of-the-art approach for PUB, outperforming conceptual rainfall–runoff models with traditional regionalisation approaches. However, global LSTMs are spatially agnostic, relying solely on static catchment attributes to differentiate regional hydrological behaviour. This study introduces Regionalised Fine-Tuning (ReFT), a strategy that adapts a pretrained global LSTM to the region surrounding each ungauged target catchment by fine-tuning on a spatially weighted set of donor catchments using an inverse-distance weighting scheme. ReFT is evaluated on 218 catchments from the CAMELS-AUS dataset under a spatial out-of-sample cross-validation framework, comparing two fine-tuning configurations: updating all model parameters versus updating only the prediction head while keeping the recurrent backbone frozen. ReFT improves Nash–Sutcliffe Efficiency relative to the base global LSTM in more than 66 % of catchments, with the largest gains occurring for catchments of moderate baseline performance. The ReFT framework combines the broad process generalisation of large-sample deep learning with the local specificity of regional adaptation, providing an efficient route to improved streamflow predictions in data-sparse regions.