Li et al. present an interesting study that employs deep learning models to predict groundwater levels during freezing and thawing periods, as well as to classify the underlying dynamic drivers. The paper is mostly well-written. Still, considerable issues require significant revision to make the paper clearer. The most important ones are related to the current structure; the results and discussion are in the same chapter, which is recommended for modification. The authors are encouraged to include a separate discussion section to discuss the main groundwater level types and the most significant implications from these different types. Second, some issues should be more precisely defined in the method. Finally, the authors should consider to present the conclusion in a more structured and clear way.

General comments:

1. Abstract:

It would be worth rephrasing to make the message clear and better reflect the key findings and the value of this study.

2. Method: 

a) Figure 2 What do the solid circles in Fig. 2(a) represent? Additional description on these labels should be added to the figure caption. Since some similar information is presented in panels (a), (b), and (c), consider merging some of them.

b) Lines 147-149 How do you determine the exact timing of the beginning and end of the freezing period for each well? A precise definition of the freezing period should be provided, similar to the one you gave for the ‘Beginning of winter’ in Lines 194-198.

c) Lines 167-169 Please detail the method to estimate the groundwater extraction volume. Given that the groundwater extraction volume is a key component of the proposed mechanism, its estimation accuracy may have an impact on the results. Also, the well depth and screened interval of the monitoring wells might also influence the response rate of the observed groundwater levels, but this aspect does not appear to be addressed in the paper.

3. Result and discussion: 

a) Lines 313-314 This statement is unclear or lacks significance. Could you provide a quantitative indicator to support it?

b) Lines 359 The authors are strongly recommended to label the three monitoring wells representing the three types of groundwater level dynamics (panels b, c, and d) in Figure 5a. The well numbers mentioned here are not very informative since the locations of the wells are not indicated. There are similar cases later on as well.

c) Lines 388-395 I am not sure I fully understand the authors’ meaning here. They state that continuous groundwater level decline mostly occurs in areas with deep groundwater level, but actually, the groundwater depth is greater in areas where the groundwater level shows a continuous rise. Moreover, I think some of the mechanism for the “continuous rising” type should be discussed further, that could enhance the implication of this study.

d) Line 427 It is confusing to see the sentence “Precipitation directly recharged the groundwater” here.

e) Some subheadings are a bit too long and very similar, e.g., Sections 3.2, 3.2.1, and 3.2.2, as well as 3.3, 3.3.1, and 3.3.2, I suggest the authors refine them.

f) The authors are encouraged to strengthen the discussion by connecting this research to relevant studies and highlighting its potential implications.

4. Conclusion:

The conclusion section is considerably longer than necessary and could be more concise.

Minor comments:

Line 49 There are formatting issues with some references, which also appear throughout the rest of the paper.

Line 137 delete “topography of the”

I’m not sure if it’s due to image resolution, but some of the colors in the figures are difficult to distinguish. For example, in Fig. 2a, the colors of the solid circles are too similar to those used in the base map.

This manuscript applies a machine learning (ML) approach to predict time-varying groundwater levels in seasonally freezing regions of China. The topic is timely and of high importance for groundwater resource management and environmental protection. However, the study overlooks several critical factors that could significantly influence the results and interpretations. By incorporating additional hydrogeological and environmental variables, the model's accuracy could be greatly improved, leading to a more comprehensive understanding of groundwater dynamics.

Specific comments:

• Line 25: Please define NSE upon first mention to ensure clarity for readers unfamiliar with the metric.
• Line 39: Provide more detailed justification of why monitoring groundwater levels is crucial, not only for managing water resources but also for protecting ecological systems. Additionally, consider using the ML-predicted results to present a case study with quantitative analysis to better illustrate the implications.
• Lines 62–67: The key disadvantage of physical models, compared to ML models, lies in their time-consuming setup, calibration, and validation processes. However, physical models have the advantage of offering more mechanistic insight into underlying hydrological processes, which ML models often lack.
• Line 118: The model would benefit from incorporating a wider range of influencing factors, such as aquifer properties, topography, hydraulic conditions (e.g., lateral flow, vertical leakage, groundwater storage, surface water interactions), and anthropogenic variables like population density. Spatial heterogeneity in evapotranspiration and precipitation should also be considered to improve model realism.
• Figure 2: Consider including a geological map that shows the distribution of geological formations or aquifer types. This would help contextualize the results spatially.
• Figure 4: The observed and simulated groundwater levels do not align well; the simulated series appears overly variable. Please explain the possible causes of this discrepancy, such as overfitting, lack of key input variables, or limitations in the model's temporal resolution.
• Lines 373–376 and 557–558: These sections are overly descriptive. Instead of simply stating observations, clarify what the results reveal about the status or trends of water resources. Quantitative insights or implications for water management should be emphasized.