Review comments on “Deep Learning based sub-seasonal precipitation and streamflow forecasting over the source region of the Yangtze River” 

In the submitted manuscript, the authors apply CNN-ResNet to statistically downscale S2S precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) at the source region of the Yangtze River Basin for streamflow predictions over a 30-day forecast horizon. Specifically, the S2S precipitation forecasts are downscaled, incorporating 19 additional forecast variables, to a 0.25-degree spatial resolution over the study region. The downscaled S2S precipitation forecasts are then run through the physically-based Xin’anjiang (XAJ) model and a hybrid model that combines XAJ with LSTM (XAJ-LSTM) to generate two sets of streamflow predictions. The results demonstrate that the proposed CNN-ResNet improves S2S precipitation forecast skill compared to the baseline Quantile Mapping (QM) approach. Consequently, the CNN-ResNet corrected S2S precipitation leads to more skillful streamflow predictions. A comparison between XAJ and the hybrid XAJ-LSTM indicates that XAJ-LSTM better translates the improved S2S precipitation forecast skill into streamflow predictions. Overall, the reviewer finds the manuscript to be well-written and of high potential for publication in the Journal of HESS. However, the reviewer has a few comments and suggestions that should be addressed. Therefore, it is recommended that the submitted manuscript undergo some substantial revisions. Detailed comments are listed below: 

1. Methodology (QM and the corresponding evaluations):  

Although QM is a widely applied technique and should be familiar to the general audience of HESS, the reviewer believes that some key information or references may need to be incorporated. Precipitation data is known to be highly skewed, making the selection of the distribution function critically important for the effectiveness of QM. However, such details appear to be missing in the current manuscript. Additionally, the reviewer is curious about the specific implementation of QM. Was seasonality or the variation in forecast lead times considered in the QM-based bias removal process? More detailed documentation on this aspect should be included in the manuscript. 

Furthermore, given that QM is primarily designed for bias removal rather than enhancing the temporal correspondence between forecast time series and observations, the reviewer suggests that the authors also evaluate the resulting forecasts (both precipitation and streamflow) in terms of their bias. Specifically, the overall CDF of precipitation forecasts generated by different statistical downscaling methods should be compared. Given that these downscaled precipitation forecasts eventually run through lumped hydrologic models, CDF of the areal averaged precipitation forecasts is perhaps a good way to demonstrate the bias condition at all percentiles across the study region. While the proposed DL technique improves the predictive skill of S2S precipitation, it would be valuable to see whether it also reduces forecast bias compared to QM. 

2. Methodology (Statistical downscaling of S2S precipitation forecasts):  

The reviewer feels that the description of the employed statistical downscaling techniques is unclear in general. It appears that the proposed CNN-ResNet generates a single precipitation prediction value while using multiple spatially distributed forecast variables as inputs (Figure 2). If this is indeed the case, the proposed framework seems more like an "upscaling " rather than "downscaling" technique. This also raises questions about how the authors produced the spatially distributed precipitation climatology plot (Figure 6). Additionally, given that CNN-based structures typically produce square-shaped outputs, were any masks applied during the training of the proposed CNN-ResNet?  

Similarly, is QM conducted at each pixel across the study watershed? If so, does the raw spatial resolution of the S2S precipitation forecast match that of the reference precipitation? These questions are particularly relevant considering the employed hydrologic models are lumped. It is important to clarify for the audience at which specific technical step(s) the spatially distributed forecast variables are converted into area averages. 

In general, it is recommended that the entire methodology section be revised to avoid potential confusion and to ensure clarity on the steps involved in the downscaling process. 

3. Seasonal and spatial skills and variabilities: 

The reviewer suggests conducting additional seasonal and spatial analysis to better highlight the strengths and weaknesses of the proposed CNN-ResNet downscaling technique. First, the reviewer notes that the study watershed covers a broad area (around 10 degrees, or approximately 1000 km, in both the north-south and east-west directions). This suggests significant spatial and seasonal variability in terms of precipitation generation mechanisms, magnitude, frequency, etc. However, this variability is not discussed in the manuscript, limiting the audience's understanding of the study watershed. 

Building on this, it would be interesting to examine whether the proposed framework is equally effective across different seasons and geospatial locations, or if its performance varies. The reviewer believes such an analysis would be crucial in further enhancing the quality of the manuscript. Consequently, it is recommended that the authors evaluate the post-processed precipitation both spatially and seasonally. Since the proposed method is a statistical downscaling technique, it is important to demonstrate its skill over such a large study region. This additional analysis would provide valuable insights into the effectiveness of the method across different conditions. 

4. Probabilistic and ensemble forecasts: 

If the potential workload is manageable, the reviewer strongly recommends that the authors utilize the entire ensemble of S2S precipitation forecasts from ECMWF in their experiments, rather than focusing on the ensemble means. The primary reason for this suggestion is that neither precipitation forecasts nor the corresponding streamflow predictions can be applied deterministically at a subseasonal timescale due to limited skills at longer forecast lead times. 

At this timescale, probabilistic forecasts are typically constructed using multiple predictions (i.e., ensemble forecasts). While the proposed framework appears effective and interesting, the reviewer believes its full potential can be better demonstrated with a revised experimental design that aligns more closely with real-world needs (i.e., ensemble predictions). 

Following this suggestion, the reviewer suggests the authors to incorporate additional probabilistic evaluation metrics, such as CRPS or CRPSS, for a more comprehensive assessment of the framework's performance for both post-processed precipitation forecasts and the corresponding streamflow predictions.