This paper presents a python package to implement twenty different PET methods. This package is going to be quite useful in hydrological modeling. The paper is quite well written and is easy to read. Therefore, I don’t have any major suggestions. There are a few things that the authors can add to further improve the paper:

• Add the equations of each of the 20 models in the paper or in the Appendix. Importantly, specify the units of each of the variables along with that of PET. I see that the units have been provided on GitHub, but providing the units to the paper would also be useful.
• Include the plausible ranges of ET model parameters that can be treated as free parameters. This will be very helpful for the modelers who are new to these models.
• Re-check the performance measures in the Figure 5. The R2 does not change between default and calibrated scenarios. In the lines 236, you mention that R2 increases. But it does not.

This paper presents a Python package for Potential Evapotranspiration (PET) estimation, integrating 20 different methods within a single framework. I appreciate the effort to consolidate these methods into one publicly available platform, facilitating an understanding of various PET estimation approaches for users, which aids practical applications. However, the paper lacks novel scientific contributions or innovations, particularly given the existence of R packages for PET estimation and numerous established publications in this area. 

Specific comments:

Although the authors acknowledge the uncertainties associated with different PET estimation methods and the additional uncertainties introduced by climate change, no uncertainty analysis is provided. The Penman-Monteith method is regarded as the standard. If one method is indeed the standard, the necessity of including other methods must be clearly justified.

While acknowledging the Penman-Monteith method as a standard reference, the manuscript should objectively evaluate the potential value of other empirical approaches, especially for applications in data-sparse regions or for quantifying uncertainties across an ensemble of methods.

The introduction lacks a compelling motivation for the research. It should clearly identify key remaining scientific gaps or problems in PET estimation that PyET aims to solve through new research or technical capabilities.

A detailed comparison with existing R packages for PET estimation is essential. Highlight the significant advantages of your Python package over these existing tools. Clarify the scientific contributions of PyET beyond merely including more methods.

The model description section should provide detailed information on the included PET methods, provide an in-depth analysis of the fundamental assumptions, limitations, and suitability of each PET approach for different hydroclimatic regimes and data availabilities. Clearly articulate the significant advantages offered by PyET in terms of new scientific insights, functionality, performance, or other technical merits.