Abstract
Understanding the variability and causes of extreme precipitation events is critical for effective climate risk management and adaptation to climate change. Although previous studies rely on extreme precipitation indices, they either exceed a certain absolute threshold or are based on the chance of a given quantity occurring, ignoring the inequality of projected extreme precipitation events. To fill in the gaps, this study used a group of thirteen bias-corrected CMIP6 GCMs to predict differences in extreme precipitation levels for the near future (2021-2060) and far future (2061-2100), using two common socioeconomic pathways (SSPs): SSP2-4.5 and SSP5-8.5 compared to the current period (1985-2014). The current study aims to evaluate future spatiotemporal inequality in the MME mean of extreme rainfall indices across Bangladesh using Gini coefficients (GCs) and their plausible causes of inequality in the precipitation system. The results indicated a varying increase in precipitation inequality in the near and far future compared to the baseline. In the near future, the northern region shows a significant spike in precipitation inequality (GC = 0.24), while the far future shows a slightly reduced inequality (GC = 0.22). Importantly, far-future projections suggest more potential shifts than near-future shifts in precipitation inequality. The simple daily intensity index (SDII) shows positive correlations with sociodemographic variables such as population, male, and female (r = 0.56, p < 0.01), suggesting a socioeconomic loss in the northern region. The results of the random forest model revealed that literacy rate and labor participation are critical sociodemographic factors influencing extreme precipitation in Bangladesh. Higher geopotential heights, lessened relative humidity, and increased monsoon rainfall, all connected to a decrease in surface pressure, contribute to the precipitation system's inequality, as demonstrated by the. ERA5 reanalysis datasets. The results of this study provide valuable insights into the spatiotemporal irregularity of precipitation extremes and may assist policymakers in managing water-related disasters.
