The spatial inhomogeneity of changes in global precipitation, which is directly related to extreme flooding or drought events, represents a significant feature of climate change. To date, limited studies quantified the spatial inhomogeneity of global precipitation and its long-term change. Aiming at solving the above challenge, this study introduces a novel but simple methodological framework that is able to (1) quantify the spatial inhomogeneity of global precipitation and its variability, (2) estimate contributions of different precipitation intensities and (3) assess contributions of different regions. In this framework, the spatial inhomogeneity is quantified by the spatial variance of gridded precipitation normalized by the global mean precipitation, namely spatial coefficient of variation (SCV). Then, the spatial probability density function (PDF) of precipitation intensity is introduced to identify contributions of different precipitation intervals that lead to changes in global precipitation inhomogeneity. Finally, the global inhomogeneity can be decomposed into intra-regional and inter-regional inhomogeneity components to estimate the contributions from different regions.
The result shows that the inhomogeneity of global annual precipitation has increased consistently across multiple datasets in the satellite era (1979–2021), attributed to the increasing area of both extremely high and low precipitation. Based on the Global Precipitation Climatology Project (GPCP V2.3) dataset, the increase in inhomogeneity of global annual precipitation is primarily contributed by the intra-regional inhomogeneity component of Northern Hemispheric tropical ocean (+ 60.2%) and Southern Hemispheric tropical ocean (+ 40.3%), and is partly offset by the inter-regional inhomogeneity component of Northern Hemispheric mid-latitude ocean (− 4.5%). This study demonstrates how the spatial inhomogeneity of global precipitation can be easily estimated, which is implications for quantifying, monitoring, and understanding changes in climate extremes. Our framework offers a tool for dataset or model assessment, particularly useful for the regions susceptible to extreme precipitation events.

气候变化,空间方差,空间变异系数,概率密度函数,降水