The aims of the presented study are to 1) incorporate externally-prescribed growing season data into the Community Land Model (CLM) and 2) use the modified model to explore the effects of the Global Gridded Crop Model Intercomparison (GGCMI) growing season data on historical yield and irrigation water demand simulations.

The research conducted is interesting and beneficial to the agricultural and modeling field. It sheds light on the new prescribed calendar functionality of the CLM model, but also shows limitations of the simulated global phenology results. The methodology is well described, and the conclusions are well derived. However, there are some minor issues that hinder the clarity of the study. Minor revision is recommended before acceptance.

General comments:

1. The study often refers to wheat in general (e.g., in text and figures 9 and 10 labels), but for CLM5, wheat is only represented by spring wheat. The authors mention this several times but there are still areas which may suggest that winter wheat is considered in the analysis. I also find it difficult to draw conclusions for global wheat phenology when vernalization is not considered since it plays a key role in determining planting date for wheat farmers and the length of the growing season. Further clarification should be added throughout the manuscript to ensure readers understand that only spring wheat is considered.
2. What is the reason for the huge difference in sugarcane shown in S15? Most areas are > 200 days different which is an entirely different season. Why even include sugarcane and cotton if they are not used in most global studies due to limited confidence (as stated in lines 304-308)? It is challenging to have confidence in the simulated results with this large of a difference. A detailed section on model uncertainties should be included in the Discussion.
3. The median of the prescribed calendar GDD for cotton are much higher than the CLM default in Fig. S17. This results in the large yield increases seen, but how are these GDD values justified? Additionally, the prescribed calendar GDD variation for all crops is unrealistic (e.g., < 500 or > 3000 GDD, > 5000 GDD for sugarcane), and should be checked. How are these simulated results justified? Improved justification and uncertainty explanation is needed in the Discussion to have confidence in the model results.

Specific comments:

1. Line 25-26: “…can reduce yield because they have less time to photosynthesize.” Yield reduction from temperature driven early maturity is mainly because of less time for grain filling/reproductive growth and resource partitioning. Please clarify.
2. Line 100: what is the resolution of the simulations?
3. Line 239 to 240: Remove parenthesis around sentence.

Technical Corrections:

Line 37: Please also reference the Agricultural Model Intercomparison and Improvement Project (AgMIP, https://agmip.org/) when first mentioning GGCMI since GGCMI is a part of this larger project.

Rabin and colleagues have developed a novel function that enhances the capabilities of the CLM crop module, allowing it to incorporate externally provided planting and maturity inputs. This approach holds the promise of improved accuracy and flexibility compared to the default method embedded within the CLM crop module. Moreover, the newfound ability to utilize externally sourced growing season data marks a significant advancement, potentially paving the way for the integration of CLM into the Global Gridded Crop Model Intercomparison (GGCMI). This method bears particular importance for crop models exhibiting promising potential. The authors have executed an admirable job in presenting their findings. Nevertheless, I would like to offer a few minor suggestions:

The validation of the model involved a comparison of simulated crop yields (approximately 2-degree resolution), against data from the FAOSTAT database and the EarthStat dataset on country and global scales. However, this coarse resolution introduces uncertainties. Could you please elaborate if you have conducted validation at a finer site-specific level? This would provide a more precise depiction of the new model's enhancements.

In reference to line 426, the statement "The use of more realistic growing seasons occasionally leading to decreased yield performance" suggests that certain crops may require reparameterization to accurately function during more realistic periods of the year. Could you kindly provide further details regarding the parameters employed in the new model? Additionally, was there any calibration undertaken? This information appears somewhat unclear. Furthermore, it might be beneficial to include a section that highlights key parameters and presents a sensitivity analysis, elucidating the pivotal parameters in the new algorithm.

On line 240, the parenthesis might be a typo.