We would like to thank the reviewer for their careful and constructive assessment of our manuscript. We appreciate the positive comments regarding the scientific objectives, presentation of figures and tables, and the overall clarity of our work. We also appreciate the suggestions made to strengthen the robustness of the analysis, particularly in relation to site selection, data representativeness, temporal averaging, and spatial resolution. We have addressed each point below and made appropriate revisions to the manuscript accordingly. Please find our detailed responses below.

1. Selection of Flux Tower Sites

"Using only 11 sites seems insufficient for a study area as large and ecologically diverse as Brazil... authors should elaborate more clearly on why only those 11 flux towers were selected."

We appreciate the importance of this comment. We agree that Brazil’s ecological diversity warrants a wide spatial sampling. At the time we began this study, access to high-quality micrometeorological data was limited. The 11 flux tower sites selected represent those for which full meteorological forcing data were curated and available, including variables required for ET₀ estimation via FAO methodology (radiation, wind speed, humidity, etc.). We had access to two extra sites CAX (Caxiuanã, a tropical rainforest riverine in the state of Para) and USE (Usina Santa Eliza, a sugarcane site in the state of Sao Paulo) which were rejected because they had data quality issues or lacked complete or sufficient data coverage to meet the quality control threshold.

Despite omitting some biomes, (i.e. Caatinga and Pantanal), we feel that the main largest biomes (particularly, the Amazon and the Cerrado) as well as croplands and grasslands/pastures, have been represented. This omission will be mentioned explicitly in the revised version of the manuscript as a limitation to our study, while encouraging other studies to account for these biomes if they wish to do so.

We will also provide a detailed explanation in the revised manuscript on why conventional meteorological stations (INMET) were not included. Importantly, some of the reanalysis and blended products assessed (e.g., BNMD) incorporate INMET station data in their development. Using flux tower data, which are entirely independent of INMET, allows us to evaluate these products more objectively. We highlight the use of FLUXNET towers used as an evaluation metric for MSWEP, where the authors state “The FLUXNET data were used for this purpose (evaluate datasets) because they are completely independent; they have not been used in the development of any of the P datasets” , clarifying the importance of independent observations. Note that the flux towers used in this study were not used in the original MSWEP evaluation. This again will be clarified in the revised version.

1. Dataset Size and Statistical Robustness

“Performance metrics derived from larger datasets are generally more reliable… helpful to demonstrate that, despite site differences, the results remain comparable.”

We agree and will carry out a sensitivity analysis using subsets of the longest flux tower time series (e.g., PDG, CRA, K34). This will allow us to assess how results vary with sample size. We will summarise these results and make them available as supplementary material and acknowledge this in the methodology. If performance metrics show large discrepancies this will be addressed in the discussion.

1. Temporal Averaging and Missing data bias

“It is not clear whether the hourly samples retrieved… were selected randomly… Is using only 50% of the days sufficient for monthly averaging?”

We thank the reviewer for raising this point. To clarify, our analysis was performed at daily and monthly time steps only.

With regards to the infilling, there were obvious failures of instrumentation but in some cases, there was more than one instrument recording similar measurements (e.g. global radiation in, PAR in and net radiation) these had extremely strong correlations and (in some cases) vastly increased the temporal coverage of a variable.

For conversion to daily means, we used a 50% hourly coverage threshold as a compromise between data availability and temporal representativeness. We tested stricter thresholds (e.g., 100, 90, 80, 70, 60%) and observed a reduction in the number of valid days across sites, with only marginal gains in accuracy (comparing using K-S tests to a reference mean for each variable, whilst also looking at the standard deviation). While we acknowledge the potential for bias (e.g., from systematically missing cloudy days), our sensitivity analysis indicated that the 50% threshold did not significantly affect our daily or monthly estimates. This will now be discussed explicitly in the manuscript.

1. Spatial Resolution Harmonisation

“It is unclear why no similar approach was applied to harmonise spatial resolution among the products… I am concerned about the fairness of comparisons involving coarser-resolution datasets.”

We recognise that spatial resolution mismatch can affect the results of point-to-pixel comparisons, particularly for coarse-resolution products. However, we have decided to focus on comparing the temporal performance of different gridded products against independent, high quality point observations, rather than to perform spatial interpolation or downscaling.

Importantly, even sophisticated interpolation efforts do not guarantee spatial consistency in performance. For example, Xavier et al. (2016) found that the accuracy of different interpolation methods varied substantially across Brazil, with no clear spatial or geographic patterns explaining where a given method performed best. Their results highlight the complexity of spatial error structures and suggest that resolution harmonisation may not systematically improve agreement with observations. Therefore, while we acknowledge this is a limitation, spatial harmonisation was not applied, as it could introduce new biases or mask product-specific spatial characteristics.

We will add a discussion of this limitation in the revised manuscript, along with appropriate citations (e.g., Xavier et al., 2016; Hofstra et al., 2008), and clarify that addressing spatial representativeness through interpolation or downscaling is outside the scope of this study.

We will review the full manuscript and address smaller clarity issues noted implicitly in the reviewer’s general remarks and specific comments. Furthermore, we will also ensure improved clarity and grammar.

We thank the reviewer for their thoughtful and constructive comments. We are please that the reviewer found the manuscript to be well-written and that the findings align with the stated objectives. We also appreciate the recognition of our use of MSE decomposition and the identification of ERA5-Land as the best performing product overall.

1. Use of INMET data network

“Since the authors are evaluating gridded meteorological products, a more comprehensive dataset/network, such as the Brazilian National Meteorological Institute (INMET) data, could have been used.”

We appreciate this suggestion and agree that INMET data represent and important observational resource for Brazil. However, we intentionally used flux tower data as an independent evaluation dataset (as explained to similar comments by Reviewer #1). Some of the reanalysis and blended products evaluated in this study (e.g. BNMD) already assimilate INMET data as part of their development. Using flux tower observations, which are entirely independent from these conventional meteorological networks, provides a more objective evaluations of gridded product performance. This rationale will be clearly articulated in the revised manuscript.

1. Lack of direct comparison between gridded products and flux tower variables

“Despite using flux tower data, no specific flux tower variables were tested against the gridded products.”

In this study, we focused specifically on core meteorological variables (e.g., air temperature, precipitation, relative humidity, etc.) recorded by the flux towers as the basis for comparison with the gridded products. While we did not assess flux-derived variables such as latent heat flux or evapotranspiration, we agree that doing so would offer valuable insights into how differences in meteorological forcing translate to land-atmosphere exchange estimates. We will clarify this scope in the revised manuscript and outline the evaluation of derived variables such as evapotranspiration as a key direction for future work.

1. Limited geographic coverage of flux tower sites

“Other flux towers in Brazil could have been used to cover other regions, such as the Northeast, where the climate is predominantly semi-arid.”

We agree that Brazil’s ecological diversity warrants a wide spatial sampling (as also responded to similar comments made by Reviewer #1). At the time we began this study, access to high-quality micrometeorological data was limited. The 11 flux tower sites selected represent those for which full meteorological forcing data were curated and available, including variables required for ET₀ estimation via FAO methodology (radiation, wind speed, humidity, etc.). We had access to two extra sites CAX (Caxiuanã, a tropical rainforest riverine in the state of Para) and USE (Usina Santa Eliza, a sugarcane site in the state of Sao Paulo) which were rejected because they had data quality issues or lacked complete or sufficient data coverage to meet the quality control threshold.

Despite omitting some biomes, (i.e. Caatinga and Pantanal), we feel that the main largest biomes (particularly, the Amazon and the Cerrado) as well as cropland and grasslands/pastures, have been represented. This omission will be mentioned explicitly in the revised version of the manuscript as a limitation to our study, while encouraging other studies to account for these biomes if they wish to do so.

1. Assessment of evapotranspiration as an integrated variable

“Since evapotranspiration is an important variable from flux towers and models, it would be interesting to test the precision of gridded products against a variable that takes into consideration all the base meteorological data.”

We agree that evapotranspiration is a highly integrative and policy-relevant variable. However, as noted above, the current study focused on the direct evaluation of meteorological forcing variables. A full assessment of ET would require a different methodological framework and validation against flux-derived ET estimates (e.g., via energy balance closure), which we believe to be beyond the scope of our study. We appreciate this suggestion and will explicitly mention this point in the discussion as a direction for future work.

Specific comments

Table 1 - “Please add the average temperature and precipitation.”

We will revise Table 1 to include the long-term average temperature and precipitation for each site.

Lines 165-167 – “Clarify how the linear gap-filling was applied. Did the authors apply the same methodology for precipitation?”

The gap filling method for precipitation has been explained in a separate paragraph in section 3.2, however, we will elaborate on how the linear regression was performed i.e. what variables were used for the interpolation.

Lines 255-256: “What is the reason why MSWEPv2.2 performed better for daily rainfall BDMB at a monthly timescale?”

The way we chose to lay out the manuscript was to first state the results and then discuss them in the following section. An attempt to explain this has been made in line 386 but in the manuscript, we will add a caveat “see further explanation in Section 5.2.”