GPC/m: Global Precipitation Climatology by Machine Learning; Quasi-global, Daily, and One Degree Spatial Resolution

GPC/m (Global Precipitation Climatology by Machine Learning) is a quasi-globaldaily precipitation dataset at 1° × 1° spatial resolution. It covers thezonally global domain from 40.5°S to 49.5°N for 1979-2020 and uses a 365-dayno-leap calendar. The dataset was produced using supervised machine-learning methods trained withsatellite-based precipitation estimates and atmospheric predictors, includingoutgoing longwave radiation and circulation fields from reanalysis. It wasdeveloped to provide a long, spatially consistent daily precipitation recordfor climatological analyses, including composite analysis, correlationanalysis, climate variability, and climate-change studies. The strengths andlimitations of the dataset are described in the accompanying paper. Version v1.1-2024 is a distribution and accessibility update to the GPC/mv1-2024 dataset. The scientific precipitation-field values and existing datafile names are unchanged. The data are distributed as 42 independent yearlyNetCDF files so that users can download only the years required. The versionnumber v1.1-2024 refers to the Zenodo distribution release. The main variable is `pr` (mm day-1), and the coordinate variables are `lat`and `lon`. The files use `calendar = "365_day"`; February 29 is not included. This release also provides GrADS descriptor files, SHA-256 checksums, expandeddocumentation, and a Bash download script. The yearly files can be downloadedeither from Zenodo or from the direct Tokyo Metropolitan University mirror: https://camo3climate.github.io/gpcm-precipitation-data/ https://camo.fpark.tmu.ac.jp/gpcm.html (v1-2024; old version, tentative) Users should cite the Zenodo dataset DOI and the description paper even whenthe files are obtained from the external mirror. Description paper: Takahashi, H. G. (2024), GPC/m: Global Precipitation Climatology by MachineLearning; Quasi-global, Daily, and One Degree Spatial Resolution.https://doi.org/10.48550/arXiv.2409.09639