This study with a title of “A daily and 500m coupled evapotranspiration and gross primary production product across China during 2000-2020” has been seriously reviewed. Overall, this paper is well organized, including written English, structures, and the conclusions. Importantly, I believe that the PML-V2(China) product could provide a great opportunity for academic communities and various agencies for scientific studies and applications. However, before acceptance the authors should give the reasonable explanations to the following questions. So, I would like to recommend this paper to be conducted a major revision.

Comments:

1. In the section 2.2.2, I found that the different meteorological forcings were used here, i.e., CMFD during 2000 to 2018, but GLDAS during 2019 to 2020. Although the authors compare the difference between PML-V2(China)_GLDAS-2.1 and PML-V2(China)_CMFD at the national scale. However, the author did not compare the liner trends of these simulations. Maybe, the authors could add the evaluations of the linear trends of ML-V2(China)_GLDAS-2.1 and PML-V2(China)_CMFD GPP and ET during 2000-2018 at different spatial scales (i.e., grid and national scales). Mainly because this product has a great potential to use for study the linear trends of GPP and ET by the scholars.
2. Line 180-181: The authors did not correct the energy imbalance issues within the EC observations? Although the authors stated that “correcting such a problem may also introduce more uncertainties (Foken, 2008)”, I insist to think that not correcting the energy imbalance issues would like to greatly impact the estimated ET. Because to date there were so many studies to do the correction before using the EC observations.
3. In section 2.6, the authors simply describe the calibration for the model. I think that the authors should added some necessary description about the calibration. For example, how did you determined the final parameters for each PFT? Please clarify.
4. In section 3.1, was the estimated ET and GPP based on the EC observational meteorological variables? How the calibrated model perform at EC sites when the model was run with the CMFD forcings?
5. The used hydrological sites should shown in figure 1. Considering the high spatial resolution, the validation may be better at the small basins rather the water resources regions (i.e., Yangtze River Basin, Yellow River Basin, and so on). Additionally, the linear trends of the PML-V2 ET should be compared with the water balance-based ET at the basin scale.

He et al. constructed daily and 500m ET and GPP datasets in China using PML-V2. Compared with previous products, this model outputs improved in several aspects, including 26 EC sites being used for model calibration and validation, country-specific meteorological forcing, daily data, and intra-annual dynamics for multiple ecosystems. This ambitious work provides valuable data products for assessing the carbon and water cycles in China. They may also provide guidance in agricultural production and ecosystem management. 

The authors may consider the following suggestions to improve the robustness of this manuscript:

1. Line 99, the whole name for CMFD should be provided when it is first mentioned in the text.

2. Line 144-146, this sentence is not appropriate. You may use the MODIS land cover product, but it is debatable if it has the highest accuracy in China since there are many recently released land use/covered datasets with a high spatial resolution (30m and 10m). Many MODIS products based on the MODIS land use dataset may have low credibility in regions with complex terrain such as in the Loess Plateau. 

3. Did you test the continuity between GLDAS-2.1 and CMFD? 

4. Section 2.6, the simulated model outputs were validated at the EC site-level, and compared with the publicly available dataset. How did you get the parameter set for a certain land use type? Did all the land use types have a unique parameter set? Did you run the model at each site? 

Even though 26 EC sites were used for model validation in this study,  there are regions and PFT types that suffer from lower accuracy of model results. Nonetheless,  this manuscript tries to fill the gap of lacking GPP and ET products with high spatial and temporal resolution. Therefore, I recommend it can be considered for publication after these revisions. 

Review for ESSD-2022-183

Title: A daily and 500m coupled evapotranspiration and gross primary production product across China during 2000-2020.

This study used the PML-V2 model to develop ET and GPP datasets in China. The PML-V2 is calibrated and validated based on the data from 26 eddy covariance flux towers. The GPP and ET data developed in this study are compared with other global ET and GPP products and water balance data at the regional level. This study did a good job on model validation, but there still exist some issues in this stage.

1. As a data description paper, the methodology is an important section to let the audience know how the data is developed. However, the model description is not very clear and well organized in this paper. Although the PML-V2 model is already described in other papers, I think more details are still needed and could be put in the supplementary. Whether the code of the model is open source? If yes, a link to the model program should be provided. Why the PML-V2 (China) can simulate daily scale data while PML-V2 (Global) cannot? Are there any improvements in the model?
2. The authors claimed the PML-V2 model performed better than other products. The evidence of the high accuracy of the ET and GPP mainly comes from the validation results at 26 EC sites. The 26 EC sites were used to calibrate and validate the model, while other global products did not calibrate and validate based on the same EC sites. If the PML-V2 and other products were used to compare against other new EC sites (not the 26 sites), can it still be the best one? It seems a little bit unfair to claim that this dataset is better than others when other models cannot access these EC data. I encourage authors to also publish these EC data that are used in validation.
3. According to the distribution of 26 EC sites (Fig 2), most of them are located in arid regions where ET may be low. The total estimated ET in China may be controlled by the ET estimated in the south region where few EC sites are located. There may exist large uncertainties in quantifying total ET.
4. In the discussion section, two advantages of this new dataset are provided, one is the water-carbon coupled process, and the other is more EC data help constrain the parameters. How are water and carbon coupled in the model? And why does the coupled carbon process help advance the model? There are many land surface models that couple water and carbon processes, but it is not always the case that these models performed better in simulating ET.
5. The daily data is one important advantage of this dataset. But there are no details of how daily data is better than the data at the 8-day scale.

Specific issues:

Introduction

L60. The 8-day scale data is enough to detect seasonal changes.

L68-70. Whether this dataset has a better performance in simulating WUE. Different data sources of GPP and ET do not necessarily mean high uncertainties. If a water-carbon model is used to estimate GPP and ET, other information such as nutrient limitation may be lost, therefore, the estimated GPP may not be more accurate than directly observed data.

L86. “While” is not proper here.

Methods.

L109. Please provide the name of Ec, Es and Ei.

Equation 1-6. Reorganize the description of these equations. Separate equations and descriptions rather than list them together.

L151. Is the climate data publicly available? Maybe provide a data source link.

L188. Provide the time period information of these datasets.

L190. The data source link of the National Water Resources Bulletin.

Results

Section 3.1. Instead of the detailed description of NSE change between calibration and validation, it may be better to explain the model performance in different PFTs. For example, why PML-V2 doesn't perform well on wetland.

L293. PML-V2 performs well when compared with other mainstream ET or GPP products in China. Please check through the manuscript and make it clear the model only performed better in China.

L299-300. Performs better in simulating GPP. Add GPP in this sentence.

L333. The units of change rates could be yr-2, mm/yr /yr.

L338. Use the same decimal digits.

Fig 10. What is the SD represents? SD of spatial data or temporal data within a year.

Discussion

L347. What are the time periods of these previous estimations?

L354. Rephrase this sentence.

L369-370. The global data also have many EC observation data, but not in China. These models were calibrated on a global scale, not only in China, and pursued a global optimal solution.

L374. More spatial analysis should be conducted to prove the strong ability of the model in simulating ET on the double-cropping system. Show some regional analysis rather than only using EC sites. Why PML-V2 can have this ability?

L395. Which results in this study exhibit a huge potential for carbon sequestration of vegetation in China?

L454. How this misclassification issue was dealt with in this study.