Dear Editor,

In the manuscript ‘A global hourly ISIMIP3 climate forcing dataset for impact modeling’ the authors present an hourly climate dataset that temporally disaggregates the ISIMIP daily climate datasets using a previously published disaggregation tool. In addition, several examples are provided illustrating the impacts of using hourly data versus daily data for impact modeling. The manuscript is well written, the data is useful for impact modeling, and the dataset is readily available. However, there are a few points where the authors could provide further discussion. Therefore, I would recommend minor revisions for this manuscript. Moreover, if possible, I would recommend that the authors provide 3-hourly and 6-hourly aggregations of the dataset. Below are my main comments, along with a line-by-line list of comments.

Further discussion

There are two points that require more detailed discussion. (1) The current methodology assumes that regional and seasonal daily cycles remain valid under future climate scenarios. As this assumption is most likely not the case, several questions remain to be discussed. How does the selection of unique days change over time, and how does this change the daily cycles? What are the limits of your methods, and when should users revert to the hourly CMIP6 datasets? (2) The manuscript states that the disaggregated dataset should not be used for flash-like floods due to the spatial resolution. At the same time, the introduction mentions potential improvements for hydrological model flood simulations. The discussion should harmonize these points and include a reflection on whether the WFDE5’s hourly precipitation intensities actually match the observations.

Dataset aggregations

Although the current dataset sufficiently provides sub-daily climate data for impact modeling, providing 3-hourly and 6-hourly aggregations of the dataset could improve the dataset’s usability and uptake. Even though climate variables are well-compressed, they each use approximately 100 GB for the historical and 150 GB for the future scenarios. Many impact models operate on sub-daily, but not hourly, timesteps, and could substantially reduce storage and post-processing requirements if aggregated timesteps are provided. Note that I understand that this dataset aggregation is not necessary and involves substantial additional processing. Rather, it is my personal recommendation.

Line-by-line comments

Line 18: More examples could be added from the first line of the abstract.

Line 64: The ISIMIP3a section contains a lot of details, whereas the ISIMIP3b section does not. Some more information could be provided on the setup of CMIP6 and the selection of climate models.

Line 87: Why do wind speed and precipitation perform worse?

Line 217: Could the current methods be expanded to incorporate spatial sliding windows for the daily cycle selection?

Publisher’s note: this comment was edited on 15 July 2026. The following text is not identical to the original comment, but the adjustments were minor without effect on the scientific meaning.

Temporal disaggregation of daily climate data into hourly data is important for capturing short-duration extreme events and supporting multi-sector impact assessments at the global scale. This represents the main original contribution of the data paper. The method reconstructs sub-daily diurnal patterns by identifying the most similar meteorological day in a historical reanalysis dataset. Because the same historical analogue day is used across variables, the temporal dependence between meteorological variables is preserved, which also supports cross-sectoral impact assessment. This is an additional strength of the work.

While this is an important and valuable contribution, several points would benefit from further clarification or discussion.

1. Line 70. The parallel implementation of the TEDDY tool using zonal strips to reduce computational demand may not guarantee that the same most similar meteorological day is selected across adjacent zones for a given calendar day. This could introduce spatial inconsistencies in the reconstructed sub-daily patterns. The authors should clarify how this issue is handled or assess its potential implications.
2. The use of historical reanalysis data to identify the most similar meteorological day implicitly assumes that climate change will not substantially alter existing sub-daily variability patterns or generate new ones in the future. This assumption should be discussed as a limitation of the approach.
3. Line 160. More recent global wind-power potential assessments already use hourly wind speeds from reanalysis datasets rather than daily averages or six-hourly data, for example: https://www.sciencedirect.com/science/article/pii/S0360544217308095. The citation and discussion should therefore be updated. In this context, the disaggregated hourly dataset may be particularly valuable for improving consistency between renewable-energy potential assessments and climate-impact assessments.
4. The manuscript states several times that incorporating sub-daily variability is essential for representing nonlinear processes. However, this benefit is not directly demonstrated in the four illustrative applications. It would strengthen the paper considerably to include a comparison showing how estimated impacts differ when using hourly rather than daily data. At minimum, this issue should be discussed in greater depth.

For wind energy, the conversion from wind speed to wind power is strongly nonlinear and is approximately cubic below the rated wind speed. As a result, estimating wind generation from daily mean wind speed may underestimate output relative to calculations based on hourly wind speeds.

The solar-energy example assumes a horizontal PV panel, whereas real PV systems are typically tilted. This simplification may mask important nonlinearities in PV conversion. A more realistic assessment would require decomposition of surface downwelling shortwave radiation (rsds) into direct and diffuse components, followed by transposition to the plane of the array. These processes cannot be represented adequately using daily mean data alone.

In addition, I encourage the authors to complete the development of the disaggregated hourly dataset for offshore areas. This would be particularly valuable for energy applications. Given the growing role of offshore wind in the energy transition, realistic representation of fixed and floating offshore wind resources and their generation profiles is increasingly important for energy-system modelling. Without consistent offshore coverage, the applicability of the dataset may be constrained, as both onshore and offshore resources should ideally be represented within the same modelling framework. The authors could consider using raw ERA5 data as the offshore reference dataset, even though it is not bias-corrected over sea areas.