This manuscript is dedicated to exploring the use of hexagons to represent irrigation maps and identify irrigation drivers, which is interesting. However, this study has significant flaws. First, area deformation is more severe at high latitudes, but the global irrigated area is more distributed in the mid-latitudes and this effect would not be significant. Second, I think there are currently feasible ways to correct for the effect of latitudinal differences on area when counting global land averages. Third, the hexagon used is too large, too low resolution, and contains a large error. It is difficult to compare it directly with higher resolution grid data. Fourth, the importance of the random forest variables is strongly dependent on the data resolution, and the importance of variables with low resolution may be underestimated. Fifth, the sample sizes are an order of magnitude different when comparing precision in scatterplots, and the supposed improvement in precision derived in this way is not necessarily reliable. Sixth, the writing in this manuscript could be improved. For example, it is not necessary to write the results and findings of the study in the last paragraph of the INTRODUCTION.

The manuscript "Drivers of global irrigation expansion: the role of discrete global grid choice" discusses the adoption of hexagonal cells, as an alternative to the traditional longitude-latitude grid for global statistical irrigation modeling. The authors suggest that the new method can mitigate the area distortion and increase predictive accuracy, and the random forest method was used to identify the potential drivers of historical global irrigation expansion. Overall, this study represents an attempt to address the challenges associated with traditional grid systems in global irrigation analysis. However, there are significant areas that require further investigation to fully substantiate the authors' claims, particularly the potential bias introduced by grid cell size, which may make the comparison less fair.

Specific comments:

1) I think there seems to be a disconnect between the discussion of grid bias and the examination of global irrigation drivers in the Introduction. It would strengthen the manuscript if the authors explicitly linked how grid biases might affect the analysis of irrigation drivers, especially given the fact that significant irrigated areas are concentrated in low to mid latitudes where these biases are less pronounced (as shown in Fig. 1).

2) For the data, LPJmL primarily uses biophysical inputs. If socioeconomic factors significantly influence irrigation practices, and are not included in the model's parameters, how can the simulation data be used to study the socioeconomic impact? Also, is there any validation of the LPJmL results with observational datasets?

3) The detailed description of the random forest algorithm (like Figure 2 and Algorithm 1) might be unnecessary if no modifications were made to the algorithm in this study, given the popularity of random forest in the geoscience literature.

4) For the results in Figure 3, given the difference in grid cell sizes between the longitude-latitude grid and the ISEA3H grid, I am concerned that the larger grid cells in the ISEA3H model may be smoothing out prediction errors that are apparent in the finer-scale longitude-latitude grid. Therefore, an analysis of how grid cell size might affect apparent accuracy would be appreciated. In addition, could you provide R² values for the predictive performance of each grid model, in addition to RMSE and NRMSE?

5) The same would apply to variable importance. Different grid sizes could lead to different interpretations of what is most important in determining irrigation expansion, potentially biasing the model results. In other words, the difference between the two grid systems in the study may be the result of different grid sizes. Therefore, it is worth investigating how the grid cell size may affect the importance of variables in the random forest model.