Overall, this is a very interesting study considering how to integrate deep learning models with process-based models. The paper introduces a Spatially Recursive (SR) model based on GRIP-GL data. The model first trains a basin LSTM and then uses the trained LSTM to simulate the flow of subbasins. Finally, it obtains the ultimate basin flow through a Routing-only mode. While the aspects mentioned in the paper are not individually new approach, this combination demonstrates a certain level of innovation.

1. The "Routing-only mode" is an important part of the document, and perhaps a flowchart would help readers better understand its workflow. Additionally, the document mentions that the input for the Routing-only mode is hourly data. The question is how the authors transform data from the LSTM into hourly data.
2. Including a map of the study area would indeed be beneficial for readers unfamiliar with the GRIP-GL project.
3. The paper mentions that initially, basin data is used to train the LSTM, which is then applied to predict streamflow in subbasins using the subbasins' input data. Since there is sufficient subbasin data available, the question is why not directly train a subbasin-specific LSTM for predicting subbasin streamflow, which could then enable the prediction of basin streamflow through Routing-only mode?
4. In the discussion in section 3.1, for smaller basins, the spatial segmentation might still represent a sub-basin. In this scenario, is the structure of the Spatially Recursive (SR) model still the same as it would be for multiple sub-basins, or is the Routing-only mode not used under these conditions?
5. What is the role of Figure 8? Providing different delineations of the routing network within a single basin might better help in understanding the impact of routing network delineation.

Thank you for letting me review your paper. Please see my supplemental material for my full review.