Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India

The study analysed the spatiotemporal variation in extreme precipitation and temperature at the daily scale across India using eight indices of climate change suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this analysis, latest high-resolution India Meteorological Department (IMD) data for the period 1971–2017 (precipitation) and 1971–2013 (temperature) are used along with global gridded reanalysis products. The trends are evaluated using non-parametric Mann-Kendall (MK) test and regression analysis. At the annual scale, about 13% of the locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices for the future (using CanESM2 projected data for RCP8.5 after suitable bias correction) show significant increasing (decreasing) trend in warm days (cold days) in most (49% to 84%) of the locations. Further, most locations (varying from 60 to 81%) show an increasing trend in warm nights and a decreasing trend in cold nights. Similar analysis for the historical and future period are also performed using Climate Prediction Centre (CPC) reanalysis data as the reference and the trends, on comparison with IMD data, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.