| 20 Apr 2026
A global hourly ISIMIP3 climate forcing dataset for impact modeling
Abstract. Sub-daily climate data are increasingly important for climate-impact assessments because many processes, such as heat stress, hydrological extremes, land–surface energy balance, and renewable-energy production, respond non-linearly to intra-day variability. Daily data miss short-duration events and obscure sub-daily inter-variable interactions, creating biases in impact estimates. To address these limitations and provide consistent forcing across sectors, we generated a global hourly climate dataset by temporally disaggregating the Inter-Sectoral Impact Model Intercomparison Project Phase 3 (ISIMIP3) daily climate archives using the Temporal Disaggregation Tool (Teddy). The approach uses analogue-based hourly profiles from the bias-corrected WFDE5 (WATCH Forcing Data methodology applied to ERA5) reanalysis, preserves daily mass and energy, and maintains temporal coherence between variables. We illustrate the utility of the hourly data with four applications using the MPI Earth System Model (MPI-ESM) under ScenarioMIP pathway SSP3–7.0: (1) the fraction of wet hours, revealing rainfall intermittency not captured by daily wet-day metrics; (2) the number of hours with dangerous heat-index values, capturing joint diurnal cycles of temperature and humidity; (3) hours with wind speeds suitable for onshore wind-power generation; and (4) photovoltaic power potential calculated from radiation, temperature, and wind speed at hourly resolution. We discuss the benefits of preserving inter-variable timing, along with limitations such as reduced spatial coherence at sub-daily scales and potential constraints under strong climate-change signals. The resulting hourly ISIMIP3 dataset provides a harmonized foundation for more realistic sub-daily climate-impact modeling across sectors.
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?