Dataset Overview

The Global Precipitation Climatology by Machine Learning (GPC/m) is a comprehensive precipitation dataset produced using machine learning techniques. Key features of this dataset include:

• Temporal Coverage: Daily data from 1979 to 2020, with plans for updates to the present.
• Spatial Resolution: 1° × 1° grid, covering quasi-global regions from 40°S to 50°N.
• Methodology: Utilizes three machine learning methods, incorporating outgoing longwave radiation (OLR) and atmospheric circulation data from reanalysis.
• Formats Available: Network Common Data Form (netCDF) and Grid Analysis and Display System (GrADS) formats.

This dataset aims to provide a consistent and comprehensive precipitation record, facilitating studies in climatology, climate variability, and climate change. It is particularly useful for statistical analyses such as composite and correlation analyses.

For more detailed information, refer to the preprint paper: Takahashi (2024).