This paper motivates that the assessment of decadal climate predictions is essential to providing reliable information on hot-dry compound extremes because of their potential impacts on environments and societies. There is mention that previous research has focused mostly on climate variables or univariate climate extreme prediction at this timescale and this study aims to fill that gap for hot-dry compound extremes. It evaluates the ability of the CMIP6 multi-model decadal climate hindcasts in predicting hot-dry climate extremes as well as hot and dry univariate counterparts for forecast years 2-5, investigates the added value of model initialization by comparing the forecasts to historical simulations, and compares the modeled correlations between compound and univariate extremes with observation-based datasets, ERA5 and GCPP-BEST (as referred to in the paper). 

While the paper addresses a relevant gap and performs a worthwhile correlational analysis, much revision is required to make this study well-explained and well-interpreted. 

Major Points -- Scientific Methods

Strengths:

• Two different CMIP6 experiment types (DCPP MME and Hist MME) are a meaningful way to explore the skill contributions for initialization and external forcing. 
• Good choice to use multiple reference datasets (GPCC-BEST and ERA5). 

Critiques:

• Model initialization details should be discussed (e.g. initialized over land/sea). Model selection and some information/justification concerning the models should be mentioned instead of simply referring to the appendix table. 
• More discussion about the potential oversimplification due to all of the ensemble averaging should be discussed. 
• "Observational uncertainty" is only quoted however, discussion is required to compare the general higher agreement with GPCC-BEST in comparison to ERA5 at least. 
• The compound extremes definition is somewhat rigid and may miss more complex co-occurrences. There should be consideration given to simultaneity of event dynamics. 
• A 3 month accumulation window is chosen and is appropriate for meteorological drought but may not capture many other drought timescales. The sensitivity to accumulation period should be discussed. 

Major Points -- Explanations

Strengths: 

• Model-dependent variability is mentioned. 

Critiques:

• Explanation of indices calculation relies on references and is otherwise unclear/not justified (e.g. why use the analysis period as reference period for TX90p and are observation years used for the comparisons aligned with forecast years 2-5?). Should include some explanation similar to references (e.g. for SPI/SPEI calculation and standardization processes). 
• Methodological details feel condensed or fragmented: 
  • Mention that PET is calculated using the Hargreaves's method but no justification is provided. 
  • Description of percentile estimation using a 5-day running window is noted but how seasons are handled should be discussed.
• Missing data mentioned in figure captions but not discussed. 
• A statement about why hot-wet compound extremes are not discussed would strengthen the focus of this analysis. 
• There is mention in the introduction that dependence among univariate variables of a compound extreme can decrease the return period of such events but this is not discussed or mentioned again any further. 

Major Points -- Conclusions/Interpretation of Results

A general comment about the statement of results: overall it reads a bit as a list, with a lack of sufficient meaningful connections made or real reasons explored for greater "skill" in particular regions compared to others. 

Critiques:

• The minimal added skill from initialization is important, if a real artifact, but under-discussed -- what does this imply for the use of decadal forecasts in operational contexts? 
• The regional and seasonal variations are mentioned as future work but some exploration into this would give more content/meaning to the study. A preliminary investigation would be interesting and relevant given the spatial nature of the data and existing literature. 
• Statements about better/under-performing regions are made but connections between them and between areas for similar analyses should be explored. These really should be accompanied by considerations of the frequency and intensity of these extreme events in those areas (e.g. high skill in California important for multi-day droughts in the region). 

Major Points -- Recommendations for Further Discussion/Improvement

• Aforementioned justifications/clarifications should be included. 
• Schematic for event selection (compound index calculation) would enhance clarity. 
• Consideration of other compound event types such as humid heatwaves, lagged dependences (or explanation for why specifically these that were chosen) would expand the framework's applicability. 
• Mention and discussion of the socio-economic impacts since the motivation centers on "high-impact" extremes but is never discussed again. Linked the compound events to impact datasets could strengthen the relevance and make the closing statement make more sense. 
  • Specific mentions of events should be made rather than "compound extremes during 2003, 2010, 2015 and 2018 in Europe stand as an example". 
• Sensitivity analysis on SPI/SPEI accumulation periods could reveal the robustness of event definitions. 
• Skillful areas should be connected to real examples to emphasize the importance of the system's performance. 
  • Similarly, discussion of how lack of skill in extreme-prone areas is a major limitation (e.g. North Africa where certain types of hot-dry events are more common). 

Minor Points

• Figures are should be made larger and less crowded. 
• This is small but there are too many references to the appendix mostly in the discussion for the ERA5 results but not presented in the body. 
• Bias correction and model drift should be noted. This is mentioned in one of the authors' other papers referred to in the Data and Methods section. 

Grammatical/Written Structure

• Generally clear, scientific tone and use of domain-specific terminology. 
• Good integration of citations. 
• Minor/spelling/grammatical errors. 
• Incorrect figure references (e.g. "Figure 2d" instead of correct "Figure 2c"). 
• Text contains many run-on sentences which can be very confusing and seemingly contrasting to statements made before or after. 
• Paragraph transitions need to be made smoother and often miss key points -- topic sentences should be prominent with a clear message while more concluding sentences need to be added to complete the idea of the paragraph.