Nguyen, ChiVaze, JaiMateo, Cherry May R.Hutchinson, MichaelTeng, Jin2026-02-272026-02-271027-5606WOS:001656794800001https://hdl.handle.net/1885/733806716High-quality rainfall data are crucial for various climatological and hydrological applications, especially in detailed modelling at fine temporal and spatial resolutions. However, obtaining precipitation data with fine spatiotemporal resolution is often challenging due to the limited availability of sub-daily point measurements and the sparse distribution of rainfall stations in many regions. This paper presents and demonstrates a method to generate the Commonwealth Scientific and Industrial Research Organisation Hourly Rainfall (CHRain) dataset, which provides hourly and 1 km gridded rainfall surfaces for hydrological/hydrodynamic modelling. The method applies thin-plate spline interpolation to generate rainfall surfaces using hourly input time series obtained from hourly rainfall stations, and from daily data disaggregated into hourly intervals based on patterns observed in nearby hourly rainfall stations, and also guided by continuous radar images. The method is used to represent rainfall patterns and amounts from 2007 to 2022 in the Richmond River catchment in New South Wales, Australia. Our analysis shows that the performance of the spline interpolation improves with the inclusion of the elevation data. Larger rainfalls responded more sensitively to changes in topography, with an optimum supporting DEM horizontal resolution of around 5 km, in agreement with previous studies. Performance was also significantly enhanced by using a stable spatial occurrence analysis to reliably remove false zeros from the data. About 0.26 % of the data were found to be false zeros. During the 2017 event, CHRain achieved a correlation coefficient of 0.949 against hourly gauges, showing that the dataset can adequately reproduce the patterns of hourly rainfall measurements. The spatial and temporal analyses indicate that the CHRain dataset outperforms other gridded datasets currently available in Australia in representing the sub-grid distribution, the daily and hourly variation of rainfall across the study area, and the high rainfall values. These are all essential for capturing the spatiotemporal characteristics of flood inundation in the study area, which is frequented by disastrous flood events.We would like to thank the Bureau of Meteorology for providing the measurements at rainfall stations and the radar data. We thank the Rous County Council for providing the hourly rainfall measurements at Rocky Creek Dam and Emigrant Creek Dam during the 2022 flood event, which are critical inputs for this analysis. We would like to thank other team members at CSIRO, including Julien Lerat, Bill Wang, Steve Marvanek and Catherine Ticehurst, for downloading and preparing the data used in the paper. We appreciate the comments and suggested ideas from all three reviewers. The contributions significantly strengthened the analyses in the final version of the paper.22en© 2026 Chi Nguyen et al.ForecastsPrecipitationRadarReanalysisResolutionScaleStepTemperatureUncertaintyVariability2026-01-0910.5194/hess-30-45-2026105027300527