Guo et al. have developed a novel machine learning model that can reconstruct the global monthly burned area at a spatial resolution of 0.5°×0.5° from 1901 to 2020. This model can be used to provide a benchmark for historical simulations of fire modules in Dynamic Global Vegetation Models (DGVMs). This approach employs various machine learning models to distinguish between extreme large fires and regular fires, using climate, vegetation, and human activities as explanatory variables and satellite-based burned area (FireCCI51) as the target variable to build the models. The results of model show high accuracy in some regions when compared with charcoal records. The manuscript is well-written and the results are effectively presented. In general the work is worthy publication in ESSD. I have only minor comments mainly for clarification.

Regarding the methods:

1. Machine learning is a data-driven model, and thus the selection of data, especially the choice of explanatory variables, is crucial for model training. In this paper, variables related to climate, vegetation, and human activities were selected, and feature selection was employed to screen these variables. However, lightning, particularly cloud-to-ground lightning with sufficient energy, which is an important ignition source, was not included as an explanatory variable in the training of the machine learning model. Additionally, large-scale climate forcing, such as sea-surface temperature, can dominate extreme fire activity and the seasonality of fire activity. I would like to know the authors’ thoughts on whether these data were excluded due to insufficient temporal coverage or were screened out through feature selection.

1. The terrain can affect the spread of fire. However, among all the explanatory variables listed in Table 1 and Figure S8 in the article, there was no information on terrain. I guess this might be due to limitations in the availability of data?

1. It is unclear for me whether you do training-validation separately for each GFED region or you just do training-validation for NHAF and then apply the model globally? I.e., did you build a single model by using NHAF followed by its application everywhere? Or each region has its own model?

1. Why not consider using different types of LM model for different GFED regions if we can pick a best type of model for each region? Is this because of computation resource limitation?

1. I understand that two types of ML models were built: classification model for extreme fire grid cell and regression model to predict BAF. My question is, if a certain grid cell was classified as an extreme fire grid cell, how its BAF was determined? The grid cells with greater than 90^th quantile was classified as extreme fire but still, we need to know its specific BAF?

1. Although fire is prevalent, I guess there are many zero-BAF grid cells compared with relatively small number of grid cells with BAF>0? Did you encounter the issue of imbalanced sample size? Like there are many pixels without fire but only a small fraction with fire, will this have an impact on the model building?

1. Line 172-175: could the authors give more details on how model parameter optimization was made and which parameters have been optimized? This part is interesting. Is it a 5-fold cross validation or a circular process?

1. Line 176: After the 80%-20% 5-fold CV, here I think we call it ‘model evaluation’, which is better than ‘model validation’. We cannot really ‘validate’ a model.

1. Line 183-184: here is confusing. I get confused by whether the model was validated by 80%-20% as in line 172-175 or by leave-one-year-out?

Other minor comments:

1. On lines 161–162 of the manuscript, the authors stated that LSTMs have the best performance among all the machine learning models used in this study, and this result was presented in Fig. 2k and Fig. S7. As far as I understand, Fig. 2 was intended to display the results of LSTMs, but the description in the figure title was unclear. I would like to see a clearer statement and further explanation of what the “absolute and relative difference” mean in Fig. 2b.