In the manuscript entitled ‘Seamless climate information for the next months to multiple years: merging of seasonal and decadal predictions, and their comparison to multi-annual predictions’, Delgado-Torres and colleagues evaluate the added value of seamless forecasts from multi-annual predictions and from several methods based on constraining large ensembles of simulations, in comparison with seasonal and decadal prediction systems, as well as with ‘non-initialized’ large ensembles of historical simulations, to predict the Niño 3.4 index and spatial fields of temperature, precipitation, and sea-level pressure. Overall, I found that the authors carried out interesting analyses that highlight the relevance of both multi-annual predictions and constraining methods, the latter being a cost-effective alternative that can be updated much more frequently. I have some minor issues and comments, especially regarding the evaluations.

Title :

I found the title not very clear. I am not sure that the term ‘merging’ is appropriate here, as there is no actual merging of seasonal and decadal predictions in the study, but rather a constraint of decadal predictions and historical simulations based on seasonal predictions. If you used the term ‘merging’ in the sense of combining different data, then ‘blending’ may be more suitable.

Introduction :

l.38-41: I wouldn’t describe the methods cited here as ‘temporal merging’ methods, since they do not merge time series (this approach is not used in these studies). Indeed, they use observations or decadal predictions to constrain large ensembles of non-initialized historical simulations. The term ‘temporal merging’ is more consistent with the study of Befort et al. (2022), cited on line 50, where historical simulations and decadal predictions are concatenated.

Data :

l.96 : The term ‘climate projection’ with HIST as a reference is misleading, especially since there are not only climate projections but also historical simulations.

Method:

Fig S1 : What does « accum » mean ?

l.110 : Can you provide more explanation on the « bias adjustments (correcting both the mean and variance) »

Results:

Fig S3b : It is confusing for the November initialization that the skill from DP just after initialization (dark green), which starts in January as indicated in the legend, is shown as starting at the same month (0) as the other dataset that begins in November. Shouldn’t it instead start at month 2 to be consistent with the other dataset?

l.217-219 : Indeed, this is not a very fair comparison with decadal predictions. It would be preferable to use the same representation as in Fig. S3B, based on the DP system initialized in November.

Fig S4 and S5 : As in my previous comment, it would be preferable to also include the DP system initialized in November for the November forecast in the Figures.

l.223-224 : If I understand the method correctly, the selected members from the DP ensemble are also initialized 5–7 months prior for the May forecast and 10–12 months prior for the November forecast. It would be interesting to see whether selecting members from the DP system initialized in November of the same year of the forecast could increase the skill in Fig. S4b.

l. 224-227 : The fact that some methods using only HIST show such poor skill suggests that the predictor used for the constraint provides no information on the evolution of El Niño. Conversely, methods with skill comparable to SP and MP in the first forecast months appear to rely on more informative predictors. Are these best methods based solely on the Niño 3.4 index? And is there a common predictor among the worst methods as well?

l. 239-240 It seems from these figures that many members are selected from two models (MIROC6 and CESM1). Do you have any thoughts on why this is the case ? Are these models better in their representation of El Niño?

Fig 6 : It would be helpful to clarify the choice of constraints for the different tests. For example, in panels 6d, e, f, is it based on HIST+DP? Similarly, for panels 6g, h, i, is it based on OBS or SP?

Fig 6 : The legend for the fifth row is unclear and quite confusing. In the legend, you describe the mean absolute error for Nino3.4 or NAO (two scores), the spatial ACC, the spatial centered-RMSE, and the spatial uncentered-RMSE with respect to TOS or PSL (is this two scores, or four if TOS and PSL are tested for both centered- and uncentered-RMSE?). However, only four distributions are highlighted in the figure to test the scores, with, for example, only one as the error index, which I assume corresponds to the mean absolute error — but is it for Nino3.4 or NAO? Is one missing?

small correction:

Fig 1 : It is hard to see the brown HIST line over the purple lines. For the legend, it would be more convenient for the reader to indicate the period over which the skill is calculated, so that this information is available directly in the legend.

Fig S3b : the dark green line is missing in the Figure legend below the x-axis.

l.210: remove the tilde over the « 1 »

Fig S6 : It would be easy for the reader to have directly _DP at the end of the models that correspond to the DP ensemble.

The article “Seamless climate information for the next months to multiple years: merging of seasonal and decadal predictions, and their comparison to multi-annual predictions” by Delgado-Torres and co-authors presents a new approach to combining different climate datasets in an efficient yet scientifically sound way. The topic itself is not new, but the way the authors describe it is quite convincing to me and could provide a good opportunity for climate services. I mainly have minor technical comments.

Title: I have no particular concerns about the title. However, the term “merging” is only used in the title, introduction, and conclusion, while elsewhere the key term is “constrained method/dataset.” It might be worth considering a rephrasing for consistency.

Data: For seasonal prediction, only one forecast system has been used. Since seasonal predictions play an important role in the constraining, it should be discussed whether the results still hold when using a multi-model system or a different forecast system.

Method section: This section is somewhat difficult to read due to the large number of numerical values and coordinates given in the text. As not everything should be moved to the supplements would it be possible to summarize it in a table (e.g., listing the regions) within the text?

Results and Discussion: The main focus here is on Figures 1 and S4. The blue, purple, and even the red lines are hard to distinguish. Please consider choosing a better color scale, especially for the purple lines—perhaps grey? Figure 7 seems to have been prepared with a different graphic tool, which gives it a clearer look, although the bright green line is again suboptimal.