Investigation of Nonstationary Association of Monsoon Temperature and Precipitation Extremes through Past and Future over East-Central India

variability of summer monsoon (June to September) precipitation is moderate across the homogeneous regions of East-Central (EC) India. Under the influence of growing anthropogenic warming, the nonstationarity of precipitation extremes is getting more pronounced. The generalized extreme value (GEV) distribution is applied for frequency, intense and absolute extreme precipitation categories using block maxima approach for modeling nonstationarity in time series by characterizing the trend in parameters of the distribution. Local temperature categories (LTCs) involve daily mean, maximum and minimum values, which are used as covariates to assess the trend characteristics in location and scale parameters. The best model is chosen from maximum counts of significant cases by applying likelihood ratio test and deviation statistics, Di, among the time varying trend functions in parameters of GEV distribution. The ensemble (ENS) is bias corrected for obtaining reliable future signals under the representative concentration pathways (RCPs). The correlation coefficient estimates significant correlation between precipitation indices and LTC covariates. The trend statistic is predominately significant for observed and future precipitation indices. Significant trend of precipitation frequency and intensity is largely found over Sikkim, Sub-Himalayan West Bengal (SWB), Gangetic West Bengal (GWB), Jharkhand, Bihar, Vidarbha and Chattisgarh. The uncertainty in estimated quantiles of precipitation extremes remains high over the Himalayan territory. The highest average design return values (ARVs) at 10-, 20 and 50-year return periods are predicted over the Himalayan territory, which are moderate to high over Orissa. The minimum ARVs spread over East Uttar Pradesh (UP), Jharkhand, Coastal Andhra Pradesh (AP) and Telangana. The change estimation is detected for the future period (SCN; 2021-60) against the control period (CTL; 1961-2005). For RCP8.5, the ARVs get more intensified under nonstationarity, besides some strong decreasing signals in Bihar under both scenarios. The higher ARVs of intense and absolute indices over parts of East UP, Jharkhand, Orissa, Chattisgarh, Coastal AP and Telangana lead to future hydrodynamical changes in catchment areas. The outcomes assist the development of significant understanding of future extreme monsoon precipitation-induced hydrological risks across EC India under warming scenarios.