general comments

The objective of the paper is very interesting and extremely useful. Although remote sensing increasingly plays and important role in monitoring the earth surface, and in particular wildfire activity, it lacks the temporal extent to bring the needed confidence of results that assess the role and interaction of fire in the earth systems. This paper addresses this gap by merging different datasets to build a comprehensive and consistent geo-database of wildfire activity over western US.  The paper reads well and is clear. In terms of presentation, the paper could benefit from a visual diagram, in the methods section, on how the datasets interact and the rules (temporal and spatial) applied to remove duplicate fire records. In addition, for a paper on a new compiled dataset, it would benefit presenting all the attributes in a table, including also the definition/characteristics that are contained in dataset attributes. This not only makes it clear what the measurements and classification are, but also quickly informs potential users on the fit-for-purpose of the dataset is for their application.

specific comments

The purpose of the paper is achieved but the work is not of exceptional quality and compleness. I was expecting that for such an important topic - where one needs to merge old and new and different information types – that the new dataset and its merging methodology would set a standard on how it could be done with the perspective that it would be regularly updated with attributes that are of important to fire managers, ecologist, fire ecologist and climate researchers, to name a few. Attributes like end-date, fire spread rate, intensity, power, landcover, and fragmentation, can be retrieved and added, especially to recent records. The dataset, although very useful, is limited to what is commonly recorded by the different datasets and struggles to show added value in terms of new information useful for different applications

The authors, although focusing on achieving the higher quality records for the dataset, do not supply a quality/confidence indicator associated with each record. Meaning that users that may want to screen the dataset to remove data that could be uncertain. The compiled dataset should offer a confidence layer. It that regard, the paper fails to set a standard for dataset compilation, and it comes across that quality is reduced to removing duplicates. The authors should consider developing a confidence indicator.

The authors used several rules to remove duplicate records when merging the different datasets. These rules are mostly based on chosen thresholds in space and time. The authors should state what is the rationale behind these, and why the chosen values.

Circular areas are never representative of fire scars. Over flat grasslands under constant conditions burn scars can appear ellipsoidal, but over other landscapes they are rarely circular. By representing them as such, the authors risk overlaying a fire scar over bare soil or water, requiring users to treat the data prior to using it when landcover is important. This kind of inconsistencies should be avoided.

technical corrections

Line 37: sentence could finish with a reference to a study supporting the statement.

Line 83-84: this sentence can be confusing, I recommend reminding that the datasets includes fires that are lower than 1km and that proportion of BA is what is recorded in at every 1km2 gridcell.

Line 96: the different class of severity is presented here, it is not clear where these come from and if the classification is retained for further use. If these are all the range of possible classes provided by MTBS, and are no longer used, I recommend removing these as it will confuse the reader expecting such a classification.

Line 157: 200 kms is a large distance for exclusion, what is the rational for it and the impact of choosing a smaller distance.

Line 162-163: I assume by “keep the first fire in the database” it is meant retaining the record with the earliest date. If so, please make it clear.

Line 330: I might have missed but it is not clear what ST/C&L stands for