Thank you for the valuable inputs and comments and for your time and effort to critically review our manuscript. We will prepare a revised version once the discussion period is over. Yet, we want to stimulate the discussion by replying to your points already at this stage.

• Bootstrapping: We highly appreciate your suggestion to use bootstrapping to derive an uncertainty estimation of sampling the GWLs. We will consider this and will update Figure 3 (and related Figs in the SI) accordingly. We will apply bootstrapping to sample the n members of each SMILE from the available ensemble members. While we aim for a large number of samples the final decision on how many samples we will draw is yet to be determined. We will then update the Figures plotting the median/mean and the 5-95 percentile (or inter-quartile) range to indicate uncertainties.
• Compatibility of SSPs: we agree that not all SSPs are compatible with the selected SSP585 scenario. Nevertheless, we argue that one advantage of the application of GWLs is constraining scenario uncertainties. While the selection of the appropriate SSP is certainly a prerequisite we decided to include different spatially explicit estimates of future population distribution here to indicate the related uncertainties. We considered this a more appropriate assessment of uncertainties for these figures. However, we will explore also including the bootstrapped GWLs as suggested.
• Literature suggestions: Thank you for highlighting the aspect of slower warming and/or stabilized climate. We will incorporate the suggested publications and expand the related sections in the discussion.
• GWL range Figure 1: Thank you for critically reviewing this Figure. The GWLs for each ensemble were calculated using a 20-year moving window. This ultimately leads to a smoothening for individual years thus reducing the overall range considerably. As we then apply the GWLs based on the 20-yr window for further calculation of our results we decided to only show these in Fig.1. Nevertheless, we will mention this explicitly in the caption to improve this aspect.

We highly appreciate the constructive comments to improve our manuscript and acknowledge the time and effort to review our submission. To stimulate further discussion, we reply already at this stage prior to revising the manuscript.

• Highlighting knowledge gap/scientific merit: Thank you for critically screening our abstract and the introduction and pointing out the need to sharpen the corresponding sections. We agree that the knowledge gaps we address in this paper are not completely coming through at this stage. We will carefully revise the sections to highlight the novelty of and the need for our study. Certainly, we will emphasize the increased assessment of uncertainties using multiple SMILEs to derive the joint emergence in conjunction with increased sampling of internal variability through bootstrapping GWL/ToE/GWLoE. Both, the abstract and the introduction will be streamlined and carefully revised accordingly. We will also revise the entire manuscript to improve the language and readability of the paper.
• Selection of climate indices: we agree with the reviewer that the selected indices differ in their type and representation. However, we rather see this variety as a strength then a shortcoming as they cover absolute (extremes), quantities and intensity of temperature and precipitation. It was not necessarily the intention of our study to compare the indices with one another and rather showcase the potential of GWLoE for them. Further, we chose these indices as they are extremely frequently applied in other studies (also in the context of GWLs). In our opinion, it is thus of particular interest to address the aspects of uncertainties around GWLs and the merits of GWLoE for these indices.
• Resampling to CanESM5 resolution: Thank you for this valid and important comment. In our study we aim to address two major sources of uncertainty: internal variability (hence the use of SMILEs) and structural uncertainty (multiple SMILEs). One of the foci of our study is the joint emergence using multiple SMILEs to increase the robustness of our results (considering also structural uncertainty) on a grid scale. This, however, requires to harmonize the SMILEs to a common grid as otherwise coarser models would be penalized for their resolution, likely skewing the results. While this step is in our opinion crucial, we acknowledge and highlight the effect of resampling order for the calculation of the indices in the discussion and the supplement already. However, we agree that our reasoning to resample is partly lacking in our manuscript at the current stage and will revise the corresponding methods section accordingly.
• GWL range Figure 1: Thank you for critically reviewing this Figure. The GWLs for each ensemble were calculated using a 20-year moving window. This ultimately leads to a smoothing for individual years thus reducing the overall range considerably. As we then apply the GWLs based on the 20-yr window for further calculation of our results we decided to only show these in Fig.1. Nevertheless, we will mention this explicitly in the figure caption to improve this aspect.

Thank you for your time and effort to review and comment on our manuscript. We will revise the manuscript accordingly in the revision stage but want to stimulate further discussion by replying to your points in advance.

• Motivation to use SMILEs for this study over ensemble of opportunity that is CMIP6: The main motivation of our study is to determine when a clear climate change signal emerges from the “noise”. It is thus essential to separate the climate signal from internal climate variability. We calculate the emergence of such signal in each member of each SMILE using a KS-test to compare the distribution of the desired time window with the pre-industrial state. Once statistical significance is reached and maintained until the end of the time series (2100) we conclude an emergence of the signal. This would be possible also with a multi-model CMIP6 ensemble. However, with one single member or respective CMIP6 model realization, the forced response cannot be cleanly separated from the internal variability of the climate. It rather just represents just one of many possible realizations. As the main focus of SMILEs is to allow for a robust representation and thus quantification of internal variability, it makes their application essential for our study purposes. We thus apply a 90% threshold for each SMILE where at least 90% of the members have to indicate emerged signals. This additional step ensures the consideration of internal variability and increases the robustness of our results.  
• Use of multiple SMILEs: The main advantage of applying the multi-model CMIP6 ensemble is the excessive sampling of structural, i.e., model uncertainty. As the reviewer states, this is not reflected if using a single SMILE. However, the importance of structural uncertainty (in particular higher GWLs / towards the end of the century) is well established in the literature. We thus argue, that it is essential to also consider this uncertainty source for our study purposes. Therefore, we present the exposure of population / area for multiple SMILEs. Our results demonstrate that this is particularly relevant for the precipitation indices, where the related exposures are more sensitive to SMILE selection (compared to temperature indices). For the analysis of the probability ratio, we argue that the application of a joint emergence (using multiple SMILEs) thus further increases the robustness of the presented results and deem the application of more than one SMILE essential. However, we agree that this aspect could be more precisely highlighted in the manuscript. We will emphasize this more specifically in the revised version.
• Uncertainty assessment: we agree that a more rigorous assessment and presentation of uncertainties is required. We thus follow the suggestion by reviewer 1 to conduct bootstrapping for the sampling of GWL and ToE. The resulting updated exposure figures will thus include the 95% confidence intervals for each SMILE.
• Calculation of ToE/GWLoE: Thank you for bringing this to our attention. Our original intention was to apply GWL as the forced response of each ensemble (that is, the respective ensemble mean). However, we agree with the reviewer that the calculation of GWLoE based on each ensemble member is much more straight forward. We will thus update the methodology accordingly. Regarding the 90% threshold: this was selected (also following established Literature) so that the majority of members have to yield an emerged climate signal. A higher threshold, e.g., 95% would, however, put too much emphasis on the rather extreme members: for the MPI SMILE the current approach requires 27 of 30 members to show emergence. Raising the threshold to 95 would then consider 29 of 30 already. However, we agree that this is a rather arbitrary threshold. In the revised version of the manuscript, we will, following reviewer 1, implement a bootstrapping procedure to provide an estimate of uncertainties. This will ensure that all members are considered in the calculation of ToE and increase the robustness of the study.
• Application of probability ratio (PR): we agree that the motivation on the selection of this method in the current version of the manuscript is not obvious. In general, we focus on the assessment of the emergence of climate signals in relation to GWLs throughout the manuscript. However, we consider shifts in the entire distribution (KS-test) only, i.e., changes in magnitude/intensity. In this section, we thus aim to address potential changes in the tails of the distribution as well as changes in frequency of extremes. We agree that multiple other options for this would be available. We selected changes in PR as most appropriate to connect with the exposure of population in the previous section. The exposure to extremes is of particular interest for various reasons (public health, urban planning, general adaptation measures etc.). Further, the PR ratio is a common framework in detection and attribution which is easy to understand and communicate. The assessment of PR and related saturation in our opinion complements the other sections in a unique way. This analysis clearly highlights the need to include incremental changes compared to fixed warming levels, due to the shown non-linear response as well as the sensitivity of small incremental changes in GWL. We will, however, revise the methods section to highlight the motivation to select PRs accordingly.