The authors present a two-step error-correcting machine learning framework, TSECfire v1.0, which is used to predict BP fires and their primary controlling factors from 1997 to 2016 in Boreal Peatlands. While the method has potential value, it is important to compare it to existing research, including other machine learning methods and data-driven fire models that have been developed in recent years. I recommend that the authors provide a more extensive evaluation of their method's accuracy and its unique contributions to the field in comparison to other existing methods. Additionally, the methodology section contains ambiguous expressions that require further clarification and specificity.

1.    The title of the study focuses on BP fires, but the paper does not provide sufficient clarification on how the method used in this study differs from other approaches used to predict BP fires. Please provide additional details to highlight the unique aspects of their methodology. In the Introduction section, there is a significant discussion on smouldering fires from BPs; but there is no mention of this phenomenon in the Method and Results sections. Can the GFED or FireCCI products distinguish between peatland fires that are smouldering or flaming?

2.    In Step One (the classification step), author presented the datasets and algorithm employed in the classification process, as well as the preprocessing methods utilized for input data. However, author did not provide sufficient information regarding how the prediction of fire occurrence was made. Please provide additional details regarding the methodology employed to predict fire occurrences. 

3.    Line 195- “Fire size that might be caused by the wrong classification (namely, no fire happens in reality) could be expressed by EPnm”. Since this analysis takes place during the prediction period, it may not be possible to determine which input variables (Xfm or Xnm) were wrongly classified if there is no real-time information on whether a fire has occurred or not. Please provide more explanation. And the meaning of True Positive (TP) in Equation 9 is not clear, it is uncertain how the authors obtained the "true" data during the prediction period.

4.    If you do not consider the fire occurrence from the Step One, just predicted fire emissions or burned area based on training datasets listed in Table S1, is there a significant difference between the predicted results and the actual fire occurrences, which resulted in this approach?

5.    Line 136- “The evaluation metrics from Step One, denoting the model uncertainties, are used at Step Two to correct fire size prediction uncertainties. The two-step ML framework is detailed in Figure 1”. Please provide more explanation about what is the meaning of the model uncertainties in Step One.

6.    Line 140- “(i.e., there are more nonoccurrence records than occurrence records)”. This sentence need clarification.

7.    Line 145- Figure 1. Please increase the size of the text for better readability (also for Figures in SI). The resolution of the images is quite low.

8.    Line 149- “An oversampling algorithm called Synthetic Minority Oversampling Techniques (SMOTE) was applied onto the training dataset to address the imbalance between the two fire occurrence classes”. Please elaborate on how an imbalance between different datasets can affect the model's performance in predicting fire occurrences, and provide more details regarding procedures of using the SMOTE algorithm to address the imbalance problem.

9.    Line 217- “…could be one primary source of feature collinearity,” This sentence need clarification.

10.    What is the meaning of “training and testing” in Section 3 and 6 of SI, please provide clarification on this matter and explain the purpose of presenting both training and testing results in the figures. Also, what is the differences between "(a-1) observed" and "(a-2) observed" from Figures S3 to S12?

11.    Figure S35 lacks axis titles. 

The authors presented a two-step machine learning framework to quantify wildfire drivers and predictability in boreal peatlands: The first step use classification models to identify fire occurrences, and the second step use regression models to the burned area and C emissions, as well as the relative importance of environmental drivers. This work tested multiple datasets and tested various ML methods. While the effort is appreciated, it lacks a clear rationale for the choice of methods and what is novel compared to previous studies. 

In its current form, the manuscript is a bit convolved and hard to digest (too much jargon and acronyms). There are redundancies in many places. The results could be further distilled to provide more insights. Analysis on a regional scale would be interesting. Do the models perform better in some regions than others? Currently, data are randomly split for training (70%) and predicting (30%); if we use recent years with mega boreal fires as predicting, how well would the models perform? 

Smoldering is a key focus of the introduction and is mentioned as a challenge in the discussion. However, it is not clear how the results are relevant to it. C emissions are mentioned, but results are not shown. 

-Line 164-166: I would start this paragraph starting from “In Step Two…”

-Line 173-200: in my opinion, this part is unnecessarily hard to follow. The essence of all these could be summarized for better readability 

-Line 335-341: reiterating introduction. The discussion is a bit disconnected from the work done here. 

Figure 1: hard to read

Figure 2: are there regional differences in their behavior? 

Figure 3: it is not a very informative plot, as they all look similar. 

Figure 4: maybe focus on the best-performing models? Or use different regional subsets of samples to see if there are differences?

Figure 5: it is not clear how the results of this paper are tied to all the processes shown here (only a tiny fraction of the variables are tested)