A large sample harmonized database of daily streamflow, meteorological data, and catchment attributes for over 28,000 global catchments

High-quality observational data is critical for driving, evaluating, and calibrating geo-environmental models, particularly data-driven models. The quality and availability of such data greatly influence the output of these models. Over recent decades, numerous national, regional, and global observational datasets have been developed for catchment-scale hydrological modeling. However, the regional and national datasets often differ widely in data sources, formats, and variables, making their use challenging and time-consuming. Furthermore, existing global datasets do not include all available data sources and thus have limited coverage. In this presentation, we introduce a harmonized, comprehensive database that amalgamates existing national, regional, and global datasets into a unified, user-friendly resource. Our database consists of daily streamflow observations, daily time series of 10 meteorological variables, climatic and physiographic attributes, and catchment boundaries for over 28,000 catchments worldwide. These catchments range in size from 2~km$^2 to 1300~km$^2 (mean 150~km$^2$) and the number of daily streamflow observations per catchment ranges from 200 to 18,000 (mean 400). The meteorological data covers precipitation, temperature, humidity, radiation, and wind speed for each catchment. We included precipitation estimates from 17 state-of-the-art products such as CHIRPS, ERA5, GSMaP, IMERG, MSWEP, and SM2RAIN. To explore the database and retrieve data, we have developed a straightforward Python-based Application Programming Interface (API). All related code will be open sourced and accompanied by extensive documentation and usage examples. We anticipate this database will be an invaluable resource for various hydrological studies, including model calibration, evaluation, inter-model comparisons, and the assessment of different forcing datasets.