This manuscript makes valuable contributions to the field of hydrometeorological disaster research. By introducing a 3D event-based perspective to analyze flood-causing precipitation (FCP) events and their associated flood disasters in China from 2000 to 2023, it addresses a key gap in existing studies that often focus on isolated precipitation metrics (e.g., daily intensity) rather than comprehensive spatiotemporal evolution characteristics. The application of the connected component 3D (CC3D) algorithm to identify independent FCP events, combined with geographical detectors to disentangle driving factors, also provides a rigorous methodological framework for quantifying the link between extreme precipitation and flood impacts. Additionally, the identification of regional differentiation patterns (e.g., ‘high impact-low loss ratio’ in Southern/Northern China and ‘low impact-high loss ratio’ in the Qinghai-Tibetan Plateau) offers novel insights for region-specific flood risk management, which is of practical significance for China’s disaster mitigation efforts. However, there are also some shortcomings that deserve improvement.

1. The manuscript adopts a CC3D algorithm for FCP event identification, but it fails to clearly elaborate on the core innovations of this algorithm in the context of the study, for example, the rationale for optimizing the 95th percentile threshold for extreme precipitation or the criteria for judging event independence. Notably, the study solely uses the 95th percentile threshold without comparing it with the fixed threshold (e.g., 16 mm/h, as defined by the China Meteorological Administration) for validation. This omission makes it impossible to verify whether the selected threshold is universally applicable to FCP event identification across different regions (e.g., the arid northwest and humid southern China), potentially leading to under-detection or misdetection of events in specific areas. 

2. Population and GDP data use only two time points (2005 and 2020) to represent annual data for 2000–2010 and 2010–2020, respectively. This simplification ignores spatiotemporal heterogeneity in population migration and economic growth (e.g., rapid urbanization in the Yangtze River Delta), which could significantly distort the assessment of human activities’ impact on flood disasters.

3. The manuscript acknowledges that hydraulic engineering (e.g., dams, reservoirs) is an important flood mitigation factor but excludes it from the driving factor analysis due to data limitations. However, large-scale hydraulic projects in China (e.g., the Three Gorges Dam) have significantly altered flood regimes, especially in SC and NC. Omitting these factors may limit the comprehensiveness of the driving factor analysis, and the manuscript should discuss how this omission impacts the interpretation of results.

4. The manuscript reports that FCP 3D features (e.g., accumulated magnitude, lifespan) have increased, while most flood disaster metrics (except direct economic loss) have decreased. The explanation attributes this discrepancy to hydraulic engineering, but no quantitative evidence is provided to support this claim. For example, could the reduction in disaster impacts be quantified by the number or storage capacity of reservoirs built during the study period? Additionally, the increase in direct economic loss is linked to economic development, but a regression analysis or correlation between GDP growth and economic losses would strengthen this argument.

5. The finding of high death rates in the southeastern fringe of TP and SWC is attributed to the lack of flood mitigation infrastructure and flash floods triggered by landslides/debris flows. However, the manuscript does not provide data on infrastructure coverage (e.g., levee density, early warning systems) or the frequency of secondary disasters (landslides) in these regions. Incorporating such data or citing relevant studies would enhance the explanation of spatial loss patterns.

6. What’s 3D characteristic in Abstract? 

7. Figure 3 (FCP identification algorithm flowchart) is poorly labeled: terms like ‘26 connectivity tracking’ are not explained.

8. Table 1 (summarizing variables and data sources) is mentioned in the text (Section 2.3) but not included in the manuscript.

9. The term ‘flood-causing precipitation (FCP)’ is used throughout the manuscript but is not formally defined. It should be explicitly defined at the start of the Methods section to avoid confusion with ‘extreme precipitation’ or ‘heavy rainfall’.

10. The calculation method for ‘accumulated affected area’ (a 3D FCP indicator) is vague, whether it refers to horizontal projection area or curved surface area (critical for mountainous regions) is not specified.

11. Page 7, Line 175: ‘the 26-connectivity searching allows that a contiguous precipitation event occurring at a grid on the current hour can move to the adjacent grids in the following hour’ is grammatically incorrect. It should be revised to ‘the 26-connectivity search enables a contiguous precipitation event at a grid in the current hour to move to adjacent grids in the following hour.’

12. Page 12, Line 295: ‘the annually mean values and trend of TC FCP events’ uses an incorrect adverb; it should be ‘the annual mean values and trend of TC FCP events.’

13. Page 15, Line 355: ‘results in economic losses of USD 57.46 billion’ lacks a clear time reference (which flood event?)

14. Figure 1’s caption mentions a study period of 2000–2023, but the subplot label (a) 2024 is confusing.

15. The abstract mentions "632 flood-causing precipitation (FCP) events" but does not specify how these events were derived from the 1,041 flood disaster records. A brief note on the event merging/classification process would improve clarity.

16. The manuscript states that code is available upon request from the corresponding author, but making the code publicly available (e.g., on GitHub) would enhance reproducibility, which is increasingly important in environmental science research.