Please see attached file for comments and suggestions.

This study uses the UK Earth System Model (UKESM1) simulation results to examine the effect of solar radiation modification (SRM) geoengineering on precipitation extremes in China. As part of the GeoMIP project, UKESM1 was used to conduct two sets of SRM simulations: stratospheric aerosol injection (G6sulfur) and solar constant reduction (G6solar). Both G6sulfur and G6solar simulations are designed in such a way that global mean surface temperature under the scenario of SSP5-8.5 was brought down to the level under SSP2-4.5. Using a set of precipitation extreme indices, the authors investigated the effect of G6sulfur and G6solar on precipitation extremes for different regions of China. The authors found that compared to SSP5-8.5, both G6sulfur and G6solar ameliorate precipitation extremes over different parts of China, but increase drought risks in some northern part of China. The authors also compared the similarities and differences between precipitation extreme response to G6sulfur and G6solar for different regions of China. The analysis of this paper itself is largely sound, but I do not recommend its publication in ACP in the present form for the following reasons:

I see little science in this study. I have to say that I have not carefully examined the Results part, which is just the description of figures with little scientific insight. What the authors did is just to compare simulated precipitation extremes over different regions of China under SS5-8.5, SSP2-4.5, G6solar, and G6sulfur. Regional climate extremes are strongly dependent on the SRM scenarios (location, timing, and intensity of SAI and solar reduction). Also, regional climate extremes are strongly dependent on climate models. If one uses another climate model and/or another SAI strategy, most results presented in this paper might be different. At least, the authors should use multiple model results from GeoMIP instead of just one model. Also, the authors should try to investigate some science underlying the presented precipitation extreme comparisons. For example, why the difference between G6sulfur and G6solar? In the present form, this paper just presents simulation results from a specific model with little interpretations. At least for me, I see little science here.     

Some specific comments 

Lines 36-59: This first paragraph of the Introduction part is very lengthy and most part is not directly relevant to the study here. For example, the detailed description of extreme precipitation in Zhengzhou and Beijing is not needed at all.

Lines 61-72: This paragraph can also be substantially shortened and combined with the first paragraph.  

Line 85: check the grammar here. ‘,the climate’

Lines 90-101: The use of ‘prediction’ in this paragraph is not appropriate.

Lines 98-99: Whether SAI would decrease precipitation depends on the scenario of SAI deployment. Also, instead of Pinto et al. 2020 and Liu and et al. 2021, more influential papers on the climate effect of SAI should be cited.

Line 120: Why only use results from a single model? Why not use multi-model results from GeoMIP?

Lines 186-187: I don’t quite understand this sentence.

Line 199: The word of ‘accurate’ is not appropriate here.  

Line 133: ‘reducing the solar constantor increasing SAI’. Check grammar and spelling here.

Lines 225-226: I don’t understand what ‘SAI is sensitive to global warming’ means.

Line 245: Where are ‘the other three G6 models’?

I think this could be a useful study with some work.  I always like creative ways of integrating measurements and models.  The analysis is also carefully done and focuses on clearly important issues (extremes).

While I don’t dispute any of the findings, my biggest issue is with the explanations.  There is a lot of reporting of the results but not much interpretation other than (sometimes) speculating about mechanisms.  Given that the authors have a great deal of climate model output at their disposal, they could look into some of these mechanisms.  I would point out specific examples, but this seems to be a general issue in Section 3.

Also, there is a lot of discussion of different indices, but they mostly show the same thing.  That’s not a problem, but the way you’re describing them makes it seem like you’re going through a laundry list of indices.  I’d like to see more insight.  Digging into the results in Table 2 would be interesting.  For example, _why_ does CWD not behave like the other indices?  What’s special about those two regions that have the opposite sign?

Figure 1 and Section 2.1:  Any reason you don’t include the Tibetan plateau?

Lines 169-182:  This seems like a long way of saying that you used survival functions, which are a perfectly reasonable thing to use for what you want to do.

Lines 186-187:  This is not consistent with my understanding of what field significance does.  I would appreciate more description as to what you mean.

Figure 2:  Can you add a panel showing the bias as a percent instead of an absolute value?

Lines 225-226:  I’m not sure what this means.  SAI is sensitive to global warming?

Line 265:  Typo (depicting)

Line 293:  Aggregated how?

Line 295:  Be more specific about “the opposite”.  Also, what are 0 values in the table?  (I can figure it out, but you need a description.)

Lines 324-325:  It’s difficult to put these numbers in context.  Is 100 mm a lot for these regions?

Line 341:  I don’t know if “effectively mitigates” is the correct phrasing.  Be more specific.

Lines 391ff:  I’ll be honest, I had a hard time with this entire paragraph.  I’m really not sure I understand it.

Line 520:  ETCCDI