Global High-Resolution Hydro-meteorological Variables Distribution Patterns

In this study, the investigation delves into the global spatial patterns of the best-fit distribution for eight key hydro-meteorological variables, including potential evapotranspiration (PET), average temperature (Tavg), precipitation (Pre), maximum temperature (Tmax), minimum temperature (Tmin), wet day frequency (WET), diurnal temperature range (DTR), and vapor pressure (VAP). Utilizing a high-resolution global monthly Climatic Research Unit (CRU) dataset, covering a period of 120 years (1901–2020), and available at a grid interval of 0.5° × 0.5°, three goodness-of-fit (GoF) tests were employed at a 5% significance level. A range of probability distributions were considered to determine the best-fit distribution for each variable. More than 70% of global regions exhibited a greater similarity with the Generalized Extreme Value (GEV), Generalized Logistic (GL), and Log-Logistic (LL) distributions for most variables, except for WET, where approximately 61% of regions favored these distributions, with 9% showing preference for the Weibull distribution. Interestingly, widely adopted probability distributions, such as Gamma, Normal, and Log-Normal were found to best fit 1–3% of global regions individually. The findings provide valuable insights into the spatial distribution patterns of hydro-meteorological variables, which are crucial for informing sustainable decision-making processes and enhancing our understanding of climate dynamics on a global scale. Additionally, the utilization of high-resolution spatial data underscores the importance of spatial analysis in addressing sustainability challenges and advancing climate research.