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While a wide range of Earth system processes occur at daily and even\udsubdaily timescales, many global vegetation and other terrestrial dynamics\udmodels historically used monthly meteorological forcing both to reduce\udcomputational demand and because global datasets were lacking. Recently,\uddynamic land surface modeling has moved towards resolving daily and subdaily\udprocesses, and global datasets containing daily and subdaily meteorology have\udbecome available. These meteorological datasets, however, cover only the\udinstrumental era of the last approximately 120 years at best, are subject to\udconsiderable uncertainty, and represent extremely large data files with\udassociated computational costs of data input/output and file transfer. For\udperiods before the recent past or in the future, global meteorological\udforcing can be provided by climate model output, but the quality of these\uddata at high temporal resolution is low, particularly for daily precipitation\udfrequency and amount. Here, we present GWGEN, a globally applicable\udstatistical weather generator for the temporal downscaling of monthly\udclimatology to daily meteorology. Our weather generator is parameterized\udusing a global meteorological database and simulates daily values of five\udcommon variables: minimum and maximum temperature, precipitation, cloud\udcover, and wind speed. GWGEN is lightweight, modular, and requires a minimal\udset of monthly mean variables as input. The weather generator may be used in\uda range of applications, for example, in global vegetation, crop, soil\uderosion, or hydrological models. While GWGEN does not currently perform\udspatially autocorrelated multi-point downscaling of daily weather, this\udadditional functionality could be implemented in future versions

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0