A Statistical Methodology for Regional Scale Future Projection of the Seasonal Frequency of Sub-daily Extreme Rainfall Events

Sub-daily extreme rainfall (SDER) events frequently lead to natural disasters, including flash floods, urban floods, landslides, and soil erosion. It is essential to make a reliable prediction of its frequency at the local/regional spatial scale for the future period, in order to devise improved disaster mitigation and adaptation strategies. It has been observed that current CMIP6 GCMs have limitations in simulating short-duration (sub-daily) heavy rainfall events, and large biases are often observed in the control run simulations compared to historical observations at various locations worldwide. Hence, there is a lack of confidence in considering the crucial future projections obtained from those GCMs as reliable. In this study, we present a novel statistical methodology for predicting the seasonal frequency of SDER for 99 river sub-basins (RSBs) in India, encompassing tropical, temperate, arid, and polar climates across various topographies. The methodology identifies the scaling relationship between the SDER frequency and the associated potential atmospheric variables/drivers for each RSB. Results indicated that the seasonal frequency of SDER scales with (i) near-surface air temperature (SAT), and (ii) moisture content in the air, which is measured by near-surface dew-point temperature (DPT). The scaling relationship exhibits an increasing (scaling) phase followed by a decreasing (reverse scaling) phase as the (dew point) temperature increases. The range of SAT and DPT in the scaling relationship varies with RSB and climate. The SAT and DPT values at peak frequency are high for mountainous areas and lower for non-mountainous areas. The effectiveness of those scaling relationships in predicting SDER frequency at the seasonal scale was assessed/validated for the recent past (1981-2020). The method performed fairly well for RSBs with non-mountainous topography and moderately well for RSBs with mountainous topography across climate zones, except for years with an abnormally high or low SDER frequency. A finer spatial-resolution scaling relationship is deemed necessary for mountainous topographies where SDER exhibits a rather local nature. In addition, the time trends in simulated and observed frequencies closely matched. The proposed methodology is applied to predict the future seasonal frequency of SDER in the RSBs for different SSP climate scenarios till the end of the twenty-first century. The performance of various GCMs in projecting the seasonal frequency of SDER is also evaluated.