In this manuscript, the authors use IPSL-CM6 simulations to the study the effects of irrigation on regional water cycle and water resources. They conduct simulations with and without irrigation from1950–2100 under historical and SSP5-8.5 scenario. They find that irrigation expansion and climate change will intensify water use and water stress, while the spatial distribution of these changes vary across regions.

This is a comprehensive assessment, highlighting the need to include irrigation in models for climate change analyses, especially in regions with extensive irrigation. The methodology was carefully developed and overall, the manuscript is well-structured. However, it requires effort on two aspects: first, the interpretation of figures and results in the manuscript is not always accurate, and second, the explanations provided lack the depth and rigor needed to support the conclusions. The comments below may help address these concerns; however, the authors need to provide a thorough explanation of their results, considering both the model limitations and how user choices affected them.

Ln 64: Please expand all model abbreviations (LMDZ, LMDZ6A, LMDZOR, ORCHIDEE, STOMATE, etc.) in this section. Is LMDZOR the coupled LMDZ and ORCHIDEE model?

Ln 92: Can you briefly describe here (or in the Discussion section when limitations are discussed), how absence of a crop phenology module affects the results? Would this result in overestimation of water use if irrigation is applied year-round?

Ln 113: Please provide a justification for the moisture deficit factor (0.9). Same for other user-defined parameters (max. irrigation per hour, root zone).

Are these parameters global or can they be defined by region, crop type, season, etc.?

Also, did you conduct sensitivity analysis of these parameters on model outputs? In subsequent sections, it is brought up that model choices effect results (e.g., Ln 317), so it is important to quantify model sensitivity to these parameters.

Ln 90 mentions that 15 PFTs are represented with different parameter values – is this referring to the irrigation scheme parameters?

Ln 116: Where all three natural reservoirs are accessible, does the model prioritize one reservoir or withdraws equally from them?

Ln 121: Would disabling adduction from neighboring grid cells result in underestimation of irrigation water availability? I understand the need for this in coarse resolution simulations, but perhaps the authors can expand on this in the discussion/limitations section.

Ln 133: What was the reason for choosing the 5-8.5 scenario? The stronger warming will amplify the differences – did you compare the results with a middle-of-the-road scenario (e.g., 2-4.5)? I understand this is in the title (“strong climate change scenario”), so new simulations are not expected, but please highlight the effects of this choice both in Discussion (Ln 393) and in the Conclusions (Ln 446). This scenario gives the upper bounds of potential climate impacts, providing the worst-case scenario, so statements like “water depletion due to irrigation is more intense in the future than historical period…” need to be carefully presented.

Ln 176: The explanation of modulation may not be clear to the reader and needs to be parsed out. “Climate change by irrigation” and “irrigation by climate change” lack clarification. Do you mean how climate change alters the effects of irrigation and how irrigation alters the response to climate change?

Ln 180: What does "hillslope flow" mean here? Earlier it was stated that there is no communication between neighboring grid cells (Ln 122), so how does hillslope effect come into play?

Ln 186: The 76% increase is based on the mean of future (2100 – 1950) and historical (1950 - 2014) periods?

Ln 187: The interpretation of Figure 2b is not clear, I do not think it highlights “no major change in seasonality”, especially for DJF and MAM seasons. Please explain this further.

Ln 197: I do not see decrease in irrigation in northern India, it seems upper Indus has some decline. It would be helpful to add watershed outlines in Fig. 3 for better reference of domains.

Ln 199: Can you elucidate what other climate factors can explain the evolution of irrigation and how? Could the differences between irrigated areas and irrigation stem from model choices (global parameters, simplified schemes, no crop phenology)? There needs to be a more careful discussion on what differences can be attributed to climate driven vs model assumptions.

Ln 209: Precipitation increases in both irrigated and non-irrigated areas, so it may not be due to the influence of irrigation alone.

Are irrigated and non-irrigated areas identified as the cumulative shaded and grey grid cells in Figure 1a, respectively?

Section 3.3: Fig. 4 is for ET and R, not for precipitation. Please revise this section and the figure.

Ln 240: Please clarify this statement: “Additionally, stream reservoirs tend to show the strongest changes in the grid cells containing the largest rivers.”

Ln 260: Again, is this referring to cumulative irrigated and non-irrigated areas? Do non-irrigated areas also include regions in the North Pole?

Ln 261 - 265: Which panels are these statements referring to? Please add respective panel with each statement.

Ln 265: This statement needs to be parsed out. What does “no major influence on the evolution over time” mean? There is a positive trend 1950 – 2100, which is also present in the non-irrigated areas. So, there are other factors driving changes in P.

Ln 288: Which panel of Figure 8?

Ln 292: I am not sure about this statement: “…whereas the increase in water resources in non-irrigated areas in the Irr simulation is explained by the increase in precipitation in those areas near irrigated zones.” This statement is somewhat misleading, implying that the changes in non-irrigated areas are influences by irrigated regions. In Fig. 7d (bottom right panel), there are P changes farther away from the irrigated areas (e.g., the Russian Tundra).

Also, please explain the changes in the ET (Fig. 7, top right panel) over Russia.

Ln 317: Percent values of what?

Ln 323: It seems the difference between irrigation and no-irrigation is trivial here. Please also refer to a later comment regarding figure 10.

Table 1: Add “land” or “regions” after Irrigated and Non-irrigated in the header. It is confusing with Irr and NoIrr. Also, instead of Irr-NoIrr, provide the values for NoIrr so that the readers can compare the magnitudes.

Figs. 1 and 3. Please add outlines of the major watersheds in these figures. It might be helpful to combine these two figures into a single 4-panel plot with irrigated areas and irrigation side-by-side.

Figs. 4, 5, and onward. Please label each panel a, b, c, … (separating irrigated and non-irrigated) and refer the respective panel in the text. I found it challenging to match statements to the correct panels.

Fig. 10: It would be helpful to combine the panels based on the three classes in section 3.5. For example, place panels b, c, d, and e first with a heading for heavy irrigation activities, followed by moderate irrigation, and low irrigation basins. It will be helpful to provide 3-4 examples for each category of basins to ensure the results are consistent based on the classification. Also, please specify what was the criteria/threshold for the three classes (Ln 312).

Review of the manuscript ‘Joint evolution of irrigation, the water cycle and water resources under a strong climate change scenario from 1950 to 2100 in the IPSL-CM6’ by Arboleda-Obando et al, submitted to Earth System Dynamics

General summary

The study of Arbeleda-Obando et al applies their recently developed irrigation scheme in a climate change study using an AMIP style simulation setup. They find that irrigation increases strongly in their future projection due to the increase in radiative forcing and land use. Furthermore, and additional to the impacts of climate change, irrigation is shown to enhance evaporation and thereby the hydrological cycle and impacts also regions outside of the irrigated areas.

This study is the logical next step after presenting the development and validation of their irrigation scheme in a uncoupled land surface simulation with fixed atmospheric forcing in their 2023 paper. It convincingly demonstrates interactions between the applied irrigation and the interactive atmosphere and highlights regions which are prone to increase in water stress and water reservoir depletion.

Detailed comments

• L105 If the energy balance is solved for the grid cell instead of the individual tiles, don’t you reduce the effect of reduced surface temperature and higher surface humidity on the latent heat flux and thereby have a higher irrigation demand than you would have if you would compute the energy balance on tile? Do you think this effect would be significant?
• L109 You spend more time explaining the climate model than the irrigation scheme. Probably this information is available in your irrigation paper already, but please add how you avoid to accidentally irrigate too large parts of the grid cell. As you don’t seem to use an individual tile for irrigated crops (line 94 mentions bare soil, forest and crops + grasses), the soil moisture deficit in a rather dry tile would constantly remain high because even if you add enough water to saturate the irrigated fraction of the tile, the mean tile state would still show a deficit in the next time step.
• L112 are the 0.65m root depth specific for crops or is this used everywhere?
• L116 you don’t mention dams as water reservoirs for irrigation. Wouldn't it be important to include these, because they are used to mitigate the seasonality of river discharge and thus would enhance water availability during dry seasons?
• L294: I would either use `strengthening effect` or `positive feedback`, but `positive effect of climate change` sounds wrong if it is about needing more irrigation
• L324: Why the time lack? I would assume irrigation happens during the summer, but then precipitation increases during winter? Could this mean that you over-irrigate during summer (due to reasons explained above) which means you start the winter with a much wetter soil than in the non-irrigation simulations although the extra water should have been mainly transpired by the crops instead of being stored in the soil?
• L421: You found precipitation increase due to irrigation mainly in regions very close to the irrigated regions. However, others studies, e.g. de Vrese et al. (2016) found rather strong remote effects for today's irrigation. Do you see these effects as well?
• Fig 3: For consistency if would be nicer if you use gray either as zero change values in your colorbar or to indicate areas without irrigation and just use the continent outline as in the b) panel.
• Fig 4: You seem to have included the wrong maps: ET and R (which are also part of Fig 5) instead of precip and temperature as written in caption and text.
• Fig 6: why a fitted polynomial surface? Looks like a curve to me.
• All Figs: all difference plots use exactly the same colorbar. While you can do so, of course, please at least mirror it depending on whether it depicts e.g. temperature is shown with colder in blue and warmer in red, which is intuitive, but for e.g. Precip an increase should be blue and not red, and vice versa

References

• de Vrese, P., S. Hagemann, and M. Claussen (2016), Asian irrigation, African rain: Remote impacts of irrigation, Geophys. Res. Lett. 43, 3737–3745, doi:10.1002/2016GL068146.