Reliable streamflow forecasting is a fundamental component of flood risk management. However, the accuracy of such forecasts in data-scarce river basins remains one of the pressing challenges in most African catchments. While Long Short-Term Memory (LSTM) networks have demonstrated their effectiveness in rainfall-runoff modelling, their data-intensive requirements limit their direct applicability in poorly gauged catchments across sub-Saharan Africa. To address this issue, we propose a climate similarity-based transfer learning framework using the global Caravan hydrological dataset to improve local streamflow forecasting in Ouémé River Basin (ORB). Specifically, tropical-climate catchments are first identified from Caravan global dataset using the Köppen-Geiger climate classification. A composite climate similarity index (CI) is constructed from two normalized climate indices: annual mean precipitation and seasonality index. 200 basins were selected and ranked by CI. From the 200 ranked basins, a fixed subset of 50 basins was selected using stratified sampling across CI quartiles, including 30 basins for validation and 20 basins for independent testing. The remaining 150 basins were then used to define three cumulative pre-training subsets of increasing size (50, 100, and 150 basins), ranked by CI. Different LSTM models are pre-trained on each subset and fine-tuned on the ORB streamflow. The developed transfer learning models are evaluated against the LSTM model trained exclusively on local data as a baseline model. Pre-training improves prediction at all five stations, raising the median Kling–Gupta Efficiency from 0.65 for the local model to 0.75 for the best transfer configuration. The smallest, most climatically similar donor subset gives the best fine-tuning performance; adding less similar catchments does not help and slightly degrades it. The benefit is largest at stations with short or event-poor records and marginal at the well-gauged basin outlet, while peak flows remain underestimated across all configurations. These results show that, for data-scarce tropical basins, the climatic similarity of the transfer basins matters more than their number, and they point to similarity-guided donor selection combined with peak-weighted or hybrid formulations as a practical route to operational flood forecasting.