The authors present an interesting study on changes in near surface wind speed over South Asia with a focus on links to internal modes of variability such as IPO and AMO. Most of the results are based on a MPI-ESM large ensemble. In the current state, an evaluation of how well MPI-ESM reproduces crucial physical mechanisms related to NSWS in South Asia is missing. The manuscript is well written and structured and after some revision would be a valuable contribution to the field.

The authors do not sufficiently evaluate how well MPI-ESM reproduces the physical mechanisms related to NSWS (and it's changes). While the NSWS of reanalysis datasets lies well within the MPI-ESM ensemble spread, this might be due to multiple compensating misrepresentations in the model. I would suggest to compare the 850mbar wind climatology as shown in fig 4c to reanalysis datasets. If possible, please also show figures comparable to 4a,b for reanalysis data. If available, it would also be helpful to refer to the literature to evaluate the representation of IPO dynamics in MPI-ESM and projected trends in the Pacific and the Indian Ocean in comparison to reanalysis and other climate models.

Concerning the regression between IPO and NSWS:

Is NSWS averaged over the region of interest?

I do not understand, why you perform the regression over the period 1974-2095. I thought that with this analysis you wanted to estimate how much IPO can influence trends over periods of roughly 30 years. The regression slope between IPO and NSWS over 1974-2095 should be quite weak and does not really represent the influence of IPO on NSWS you are interested (at least in Figure 5 a). Why don't you use a similar timescale for this regression?

Related to the precious comment, I'm wondering whether analyzing the RCP85 scenario is the right choice for studying the link between dominant modes of variability and NSWS. In RCP85 there should are strong forced changes in NSWS as well as SSTs and SST patterns. Therefore both NSWS and IPO (or AMO) are very likely changing over the time frame of a century. I assume that the detrended (part of the signal that is not forced) IPO and NSWS is used for the analysis. Is that the case? Please clarify in the methods section.

Specific comments:

L79: Are the periods for all reanalysis datasets and CRA-40 the same?

L86: Are you sure about 2006 as start date for the projections? In Figure 1 you write about 2015.

L128-129: Why do you frame it as a hiatus? I find the term "hiatus" misleading here, as the MMM should not contain any influence of internal variability, right?

L150: Please explain the "inter-member EOF analysis" in more detail. Is the EOF analysis performed on a combination of NSWS patterns and SST fields? Is it performed on NSWS fields only?

This manuscript presents a comprehensive analysis of near-surface wind speed (NSWS) projections over South Asia, with a particular emphasis on understanding the role of internal variability using a large-ensemble approach from the Max Planck Institute Earth System Model (MPI-ESM). The authors identify the Interdecadal Pacific Oscillation (IPO) as a leading mode influencing regional NSWS variability and quantify its contribution to projection uncertainties. The findings highlight the significance of the IPO in modulating regional wind speed projections, which has valuable implications for regional climate adaptation, particularly in wind-sensitive sectors such as energy production and agriculture. Overall, the manuscript is well-written and clearly structured, with robust methodologies. To be suitable for publication, the manuscript requires a minor revision before acceptance:

1. The manuscript would benefit from a clearer explanation of the rationale for selecting MPI-ESM specifically. A brief justification or evaluation highlighting the selection of MPI-ESM for this study is needed.
2. Some figures could be improved for readability and clarity. Specifically, Figure 4 would benefit from clearer vector scaling or additional annotations to help readers easily interpret the patterns described in the text.
3. On page 3, line 91, the term "member discrepancies" might be unclear; consider replacing it with "differences among ensemble members" for clarity.
4. On page 7, line 151, the phrase "internal climate variability stems" could be made clearer by briefly specifying the primary mechanisms involved.
5. The captions for Figures 2 and 3 could include brief additional details on how the EOF analysis was specifically implemented to aid readers' comprehension.

General comments

The topic—understanding the role of internal variability, particularly the IPO, in shaping near-surface wind speed (NSWS) projections over South Asia—is highly relevant and novel. Given the importance of NSWS for energy planning, air quality, and hydrology in this region, I believe the paper will be of broad interest to the climate modeling and regional impacts communities.

The authors make good use of a large ensemble and apply a clear methodology to isolate the IPO’s contribution. The finding that IPO-related variability explains a meaningful share of NSWS projection uncertainty is an important result that will help guide future work and practical assessments.

Overall, I think this is a solid and useful contribution that merits publication in EGUsphere after addressing a few minor points that would help strengthen the clarity and robustness of the paper.

Specific comments

1. Model dependency
   
   The authors are transparent about their reliance on the MPI-ESM ensemble, which is commendable. However, I think this limitation could be discussed in a bit more depth. It would be helpful to comment on whether MPI-ESM is known to have biases in its IPO representation, or in Pacific SST teleconnections to South Asia. This would provide useful context for interpreting the robustness of the results.
2. Observational evaluation
   
   The brief comparison with reanalysis datasets (Fig. 1e) is a good start, but I feel this could be expanded. A spatial evaluation (e.g. maps of historical trends compared between model and reanalyses) would give more confidence that MPI-ESM is capturing relevant regional patterns of NSWS variability.
3. Other sources of uncertainty
   
   The authors note that model uncertainty dominates total projection uncertainty (citing Zhang & Wang, 2024), while IPO accounts for ~8–15% of variability. It would help readers if the paper clarified, at least qualitatively, how internal variability and model spread compare in this study’s own results—are the two sources of uncertainty of similar order in South Asia?
4. IPO prediction prospects
   
   The paper suggests that improved IPO prediction could help reduce NSWS projection uncertainty. This is a valid point, but I would suggest adding a brief caution about the current limits of decadal prediction skill for IPO. This would provide a balanced perspective on the practical implications.
5. AMO result
   
   The result that AMO plays a negligible role is interesting but not discussed in much detail. I think the paper would benefit from adding a short paragraph offering a physical interpretation—why does tropical Pacific variability dominate NSWS trends in South Asia, compared to Atlantic modes?