Yan et al. provide comprehensive investigations on Gross Primary Production (GPP) under El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) conditions in China. Seasonal differences in GPP responses to ENSO and IOD are especially well summarised. I believe this research can be used not only to support the carbon sink component in the carbon neutrality goal but also to support agricultural productivity in China. I only have several technical corrections before accepting this manuscript.

1. L32,L33: nIOD, pIOD introduced in Abstract. I can understand the meaning for (p for positive and n for negative), but I think it would be hard to understand from readers. 

2. Similarly, pIOD already used in L85 but it is lately introduced at L88. (positive IOD)

3. L112: ERA5 needs it's full name.

4. L172, L173: ~ indicates approximately in English. It is not used to express range in English. Please replace it with –.

5. Table 1: I wondered why pIOD has just one year and nIOD has only three years. Does this mean IOD without ENSO? Then, you need to clarify.

6. L620: REA5 -> ERA5

The manuscript is comprehensive and covers the key points of the study on the impacts of El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on Gross Primary Productivity (GPP) in China. It highlights the importance of analyzing the impact of ENSO and IOD on GPP in China and reveals that the impact varies by season. Additionally, it shows that the drivers of GPP are different across seasons. However, I have a few main concerns about this paper. My specific comments are as follows:

Line 105: Please consider adding a study area section in the methods part and justify why you chose China as the study area.

Line 113: Why were surface air temperature (TAS) and volumetric soil moisture (SM) selected? Please justify these choices.

Line 118: Please add more details on how the layers of soil moisture are weighted and aggregated.

Line 122: The spatial resolution of different remote sensing products varies. Have all the RS products been resampled? If so, what is the spatial resolution?

Line 193: Please justify why you chose partial correlation analysis.

Line 233: The authors investigated the varying impacts of surface air temperature (TAS) and volumetric soil moisture (SM) on photosynthesis (GPP). The GPP was simulated using the Boreal Ecosystem Productivity Simulator model, which utilized meteorological data and evapotranspiration (ET) as inputs, both of which are closely linked with temperature and soil moisture, respectively. How did you avoid circular effects? Do these effects inflate the correlation coefficients?

Line 240: In Figures 2.4a and 2.4e, please explain why the soil moisture does not align closely with the “pcor” in the east of Guangxi Province, east of Qinghai Province, and west of Sichuan Province. What other variables could affect this?

Line 260: The pattern correlation coefficient is -0.09 in MAM, which indicates that significant predictors are missing. Please discuss why the coefficient is low in MAM in the discussion part.