I have reviewed the manuscript titled "Intercomparison of Biogenic CO₂ Flux Models in Four Urban Parks in the City of Zurich", and I am pleased to find that this study is a pioneering effort in urban carbon-cycle modeling. This study applied four models in four urban parks in the city of Zurich and used in-situ observations to evaluate the models. This novel research stands out as it applies and evaluates a range of biogenic CO₂ flux models, offering important insights into urban biosphere carbon dynamics and the uncertainty analysis for different model approaches. The study’s findings are timely and contribute to a critical need for comprehensive carbon cycle assessments within urban settings. Thus, I recommend accepting the manuscript for publication, with minor revisions as outlined below:

1. Clarify model comparison methodology (page 9): The methods section provides a detailed description of the four models used; however, a clearer explanation of the criteria and rationale for selecting each model would enhance comprehension. In other words, it would be helpful to introduce different types of models and why those four models were selected in this study.

2. Add justification for normalization in SWC analysis (page 14, line 380): The rationale for normalizing soil water content (SWC) in lawn areas is stated briefly. I suggest elaborating on this choice to strengthen the interpretation of temporal variability across models. Maybe some descriptions can be added into the method section.

3. Detail on seasonal effects in sap flow observations (page 15, lines 410–425): The observed sap flow seasonality was insightful. Expanding on how seasonal drought impacts this data would be beneficial for understanding tree physiology under varying moisture conditions. Also, it is interesting that why soil moisture was so stable from October to May with a high variability of precipitation?

4. Figure 4 (page 20, line 515): Consider specifying which of the Reco values in the figure pertain to sunny versus shaded locations. This adjustment would clarify the observed seasonal discrepancies across locations.

5. Addressing discrepancies in model outputs (page 19, line 490): While the manuscript discusses the general trends of model outputs, a summary or discussion of the causes behind significant discrepancies (e.g., between VPRM and diFUME) would provide a balanced perspective on model reliability.

6. Technical correction on figure legends (various figures): For consistency and readability, please ensure that all figure legends use uniform units (e.g., μmol CO₂ m⁻² s⁻¹ vs. g m^-2 d^-1) and that legends describe all variables used.