This manuscript presents the China New-type Power Systems Meteorological (CNPS-Met) dataset, a novel 25 km daily gridded product for the Chinese mainland covering 1980-2016. The authors validate their dataset against three existing gridded products (CN05.1, CMFD, CDMet), demonstrating superior performance for most meteorological variables. They further analyze the spatial patterns of these power-system-relevant events. Overall, the topic is highly relevant for ESSD, the methodology is sound and addresses a clear limitation of traditional optimal interpolation (OI) methods, and the dataset fills a critical gap at the intersection of meteorology and energy system. The manuscript is well-structured and the results are presented clearly. I would like to recommend an acceptance after a major revision.

Major Comments

1. Methodological Justification (Lines 216-236): The spatially adaptive optimal interpolation (OI) scheme proposed in this study is sound, which dynamically adjusts the influence radius based on local station density. While the concept is clear, the paper would benefit from a more explicit demonstration of its impact. For example, how does the resulting field vary spatially across China? This would visually confirm that the algorithm is effectively expanding the radius in data-sparse regions (e.g., Tibetan Plateau) and contracting it in data-dense regions (e.g., Eastern China).
2. Clarification on the Minimum Station Threshold (Lines 227-236): It seems that the performance of the new OI scheme is also influenced by the chosen parameters (minimum station threshold). However, the manuscript does not specify the actual value of , please explicitly state it. Furthermore, what was the rationale for specifying this threshold?
3. Verification independence (Lines 163-167): The validation uses CN05.1, CMFD, and CDMet. It is crucial to clarify the degree of independence of these datasets from the observations used to create CNPS-Met. All three validation datasets are themselves interpolated products based on station data. While CNPS-Met demonstrably performs better, the validation is not entirely independent (i.e., it's not a comparison against withheld station data). The authors should validate their results against an independent truth, or explicitly state this limitation and perhaps frame the validation more as a “comparison against existing state-of-the-art gridded products”.
4. Spatio-temporal Mismatch in Event Definition (Section 2e): This is a critical point. The high-impact weather events are defined using hourly thresholds, but the CNPS-Met dataset has a daily temporal resolution. How are these hourly events identified from a daily dataset? For example, a day with a mean wind speed of 15 m/s could still have an hourly gust exceeding 25 m/s. The manuscript must clearly explain this. Besides, is the “frequency” reported the number of days where the event occurred, or an estimate of the number of hours? This distinction is fundamental for the utility of the dataset.
5. Uncertainty in the Composite Weather Index (CWI) (Lines 322-330, Eq. 13): The CWI is an interesting metric, but its formulation needs clarification. (1) The term “” is not clear, does this represent the maximum possible value of the variable, or the maximum value observed in the dataset at that location? (2) The notation “” in the piecewise function appears to be a typo. Please clarify the condition(s) for calculating the product.
6. Interpretation of Wind Speed MREs: In this paper, the MRE is selected to validate the dataset performance. The authors note that the improvement in wind speed is “relatively modest” yet it exhibits the “smallest MREs among all meteorological variables”. This apparent paradox needs clarification. Is it because wind speed is inherently harder to improve (due to its intermittency), but also easier to estimate with low relative error because the values themselves are small? The discussion could be expanded to explain why MRE is a suitable metric for wind speed, and whether other metrics like RMSE might tell a different story.
7. Analysis of Intensity Extremes (Figure 7): Figure 7 is visually rich but the criteria for defining an "intensity extreme" at the 90% confidence level is not described in the text or caption. How is this confidence level calculated? Is it based on a statistical test of the intensity values at a grid point compared to the surrounding area, or is it simply the 90th percentile of intensity for that event type? Please add a clear explanation to the text or the figure caption.
8. Relevance of Wind Speed Height for Power Systems: The dataset provides wind speed at 10 m. However, for wind power generation, the variable of interest is typically wind speed at turbine hub height (e.g., 70 m). The thresholds for cut-in and cut-out wind speeds defined in Table 2 are based on operational standards that implicitly refer to hub-height wind speeds. Please clarify whether the wind speed provided in the CNPS-Met dataset is intended to represent hub-height conditions. If so, a detailed explanation of the methodology used to extrapolate from 10 m to hub height is essential.
9. Clarity on CWI for “Heat and Humid Environment” (Lines 484-489, Fig. 10): The manuscript states that the intensity for this event is based on the CWI, which is dimensionless. However, the definition in Table 2 is simply a threshold. How does the CWI in Eq. 13 apply here? The specific variables (n) and their scaling need to be defined for the HHE event. This is currently unclear.

Minor Comments

1. Line 56: “Chapter 1 in Xin 2023” – It is unusual to cite a book chapter like this. Please provide a proper citation for the book.
2. Line 69: “It has at spatial resolution” – Should be “It has a spatial resolution”.
3. Line 79: “699 ground-based observations” – Are these stations, or individual observation points? Please clarify.
4. Line 121: “In cases of uneven observational coverage, however, the use of a fixed radius... ” – The sentence beginning here is a comma splice. Consider: “In cases of uneven observational coverage, however, the use of a fixed radius can introduce significant errors...”
5. Line 144: “meteorological stations are densely distributed” – This is redundant with Fig. 1a. Consider removing the text or referencing the figure more directly.
6. Equation (6): The formatting of the piecewise function is not clear for me. Please confirm its expression.
7. Line 249: “referred as” – Should be “referred to as”.
8. Figure 6 caption: “The concentric circles represent different datasets (from inner to outer: CN05.1, CMFD, CDMet and CNPS- Met.” – The closing parenthesis is missing.
9. Line 431: “Northern Tibet Plateau” – While commonly used, the standard geographic term is “Tibetan Plateau”, thus, “Northern Tibetan Plateau” is acceptable.
10. Line 460: “relative weak” – Should be “relatively weak”.
11. Table 2: The table formatting is broken on pages 15 and 16. This is likely a PDF conversion issue, but ensure the final table is clean. Also, the “Impacts” column entries for extreme temperatures and ice accretion are fragmented and difficult to read. Please rewrite them as complete, coherent sentences.

Please see my review in the supplement.