The manuscript by Stacey et al. examines how vegetation responses to rising atmospheric CO₂ and climate change affect global water scarcity projections using the JULES model. The authors assess how physiological (stomatal closure) and structural (leaf area and vegetation cover) changes influence future water supply. The results suggest CO₂-induced stomatal closure increases water availability, potentially alleviating scarcity, particularly in tropical regions, while vegetation expansion may worsen scarcity in some semi-arid and arid areas. Overall, this study makes a valuable contribution to the literature on vegetation-climate-hydrology interactions, and I appreciate the authors' analysis and findings.

Broader suggestions and concerns

Since this study relies on a single model, a more detailed description of key model processes would help readers better understand potential limitations. While a full breakdown of all JULES equations is unnecessary, some critical details are missing. In particular, a brief explanation of the coupled canopy conductance and photosynthesis model from Cox et al. (1998) would be helpful, as the results strongly depend on this component. (For instance, does this simplification overestimate the physiological impacts?) The discussion on limitations (L553-564) mentions issues with stomatal conductance parameterization; rather than only stating this as a limitation, it would be more helpful to discuss how it affects the key findings.

In some cases, such as in Section 3.1, the results come across as relatively intuitive. While I understand that this section serves to introduce the different simulations and their effects on vegetation and water cycle variables, I would argue that the main novelty of the study lies in the quantification and analysis of the Water Scarcity Index (WSI). For that reason, Section 3.1 could be streamlined or more tightly focused, allowing the reader’s attention to shift more quickly toward the WSI results and their broader implications.

The discussion section contains many well-acknowledged limitations, which is appreciated. However, rather than listing them in loosely connected paragraphs, they should be structured to explicitly discuss how each limitation affects the main results. Some parts could also be shortened and streamlined, for instance, the justification for using runoff as a proxy for water availability is reasonable in the context of its role as a water supply source. In my opinion this could be shortened to one sentence.

While this study is an idealized experiment rather than a model performance evaluation, I think it’s important to acknowledge that non-linear biases can emerge when driving JULES with biased GCM inputs. Even though the input fields are identical across simulations, their biases may interact differently with the model’s internal processes. This contradicts your statement in L569-571: „Given that our study focuses on the influence of plant responses – comparing the differences from two simulations with similar biases - HADGEM2-ES and this version of JULES are deemed suitable for our purposes, despite the runoff biases.“ A brief discussion on how these biases might influence the results would improve transparency.

Minor and Editorial Comments

L16: Simplifying and breaking this sentence into clearer segments would improve its readability and ensure that the statistical comparisons are more immediately accessible to the reader.

Section 2.3 (Data): This is more of a stylistic suggestion, but I found this section somewhat fragmented and lacking in flow. A more cohesive, narrative-style format could improve readability. Consider merging some of the sub-subsections to reduce fragmentation, and integrating the definitions of key terminology directly into the text rather than listing them separately in bullet points. This would help create a smoother and more engaging structure.

Figures: The manuscript contains many figures, and some could be streamlined. In my opinion you could merge Figure 1 and Figure 3  and move it to the supplementary materials or place it in the methods section, clarifying that it shows HadGEM2-ES input data to JULES. In Figure 2, adding simulation abbreviations (e.g., S2 - S1 CLIM: VEG) in the second legend for isolated factors could improve clarity.

Tables 1a and 1b: These were difficult to interpret at first glance. It might improve clarity to describe Table 1a in more detail in the text and move it to the supplementary materials. For me Table 1b was the more informative one in explaining the mechanisms, so I suggest keeping the focus there.

L263: Subject-verb agreement: "Climate-induced changes in vegetation distribution drive LAI decrease," not "drives."

L293: The statement: “The climate-induced changes in runoff from the present (2006-2025) to the future (2076-2095) largely align with shifts in precipitation (Fig. 3; right), varying greatly in both magnitude and direction (Fig. 4a,c).” seems contradictory. If they largely align, how can they vary greatly in direction?

L308 – 310: You state that the runoff decreases are driven by CO2 induced LAI increases, but this is hard to see on the map and that raises the question of how large and significant the impact really is.

General formatting: Check for missing spaces between words, unclosed brackets, and misplaced semicolons throughout the text (e.g., L293).

Summary

The authors address a gap within literature that explores how terrestrial vegetation can modulate water scarcity in future climate and CO₂ concentrations. To isolate stomatal and leaf area responses to climate change and rising CO₂, they analyzed a set of model simulations from JULES with prescribed atmospheric forcings and CO₂. They find that stomata response can alleviate global water scarcity, particularly in the tropics and already wet regions, but can amplify water scarcity in water-limited arid and semi-arid regions. I believe this study is a valuable contribution to the scientific literature and appreciate the authors’ analysis and thorough discussion of the study’s limitations. Additionally, the use of Water Scarcity Index is especially compelling as it incorporates water demands and has direct implications for future water availability.

General comments

The authors focus on a single land-surface model with prescribed atmosphere. The authors could briefly discuss how the findings of these manuscript fit into the broader context. How can the findings from this single model study be applied to other models?

The stomatal conductance model used in JULES is the Leuning one, and it would benefit readers to include a brief explanation on the variables represented in the Leuning model and how it differs from other models (e.g., Ball-Berry, Medlyn, etc.) in formulation and impact on water scarcity.

Stomatal conductance and leaf area contributions to water metrics may have nonlinearities. Stomatal conductance is linked to photosynthesis, which then affects leaf area. The authors can consider addressing some of these nonlinearities and how they might influence results.

The use of physiological and structural response in addition to stomatal and leaf area responses in the introduction is confusing to the reader. It could be helpful to directly link physiological and structural responses to stomatal and leaf area responses. I understand that the authors define “physiological forcing” in the terminology, but the manuscript would benefit if the distinction is made more clear earlier. Additionally, does “vegetation distribution change” refer the the structural response?

Minor specific comments

Table 1a and b were very helpful in understanding the different simulations. An additional line separating calculations vs simulations would help the reader interpret the table, though I do appreciate the bolding attempts for that.

Figure 1 could benefit from removal of the legend since data from only one simulation was used to create the plots. Instead, an overall title (e.g., ‘Annual mean timeseries from S1. CLIM: STOM). The removal of the legend will also allow the plots to be larger.

Runoff as a proxy for water supply intuitively makes sense, but has not been explicitly related in the introduction or methodology section. A sentence for why runoff is used as a proxy in the methodology section would be helpful to the reader. Additionally, surface and sub-surface runoff are analysed, and a distinction and description for the two and what they can tell us would be helpful.

L360: The “decreases” in the last two sentences are not specified and confusing to the reader.

The right plot in Fig. 8 “% difference” could state “% difference of median WSI” for the reader’s ease of reading.

Technical corrections

L151: extra space after period: “bias correction .”

L231: Extra space between “SSP2” and “from”

L264: extra “;” after “Fig. 2n”

L416: Extra “, )” after “(Fig. 9d)”

Generally, double check for extra spaces, missing spaces, commas, and semicolons throughout the manuscript.