Univariate and bivariate spatiotemporal characteristics of heat waves and relative influence of large-scale climate oscillations over India

Heat waves (HWs) have grown in frequency, duration, and intensity in last few decades globally and even the latest climate projections (CMIP6) reveal these to get worsen under enhanced global warming. HWs have adverse impacts over biophysical and human systems, therefore, to analyse the impacts of HWs accurately, it is required to understand the regional trend and variability associated with HWs. We carried out a bivariate copula-based joint exceedance probabilistic analysis of HW characteristics to better assess the vulnerability of Indian regions. The regions with comparatively higher HW frequency, higher intensity, longer duration, and lower joint return period of HW frequency and intensity were selected as vulnerable to HWs. Rajasthan, Madhya Pradesh, Uttar Pradesh, Punjab, Bihar, Chhattisgarh, Gujrat, Karnataka, Maharashtra, Telangana, Andhra Pradesh, and Odisha states of India have been found vulnerable and are projected to get even more vulnerable in the future as per the recent climate projections. To unveil the spatiotemporal variability in HW characteristics, we investigated the relative influence of crucial large-scale climate oscillations (LSCOs) i.e., ENSO, IOD, and AMO on HW by introducing these atmospheric variables as covariates in nonstationary extreme value theory (EVT)-based models. The statistical analysis from generalized extreme value distribution of temperature extreme with LSCOs reveals that ENSO and IOD simultaneously have been influencing the extreme in 21.2%, 25.9% and 23.1% of total region taken into consideration in April, May and June month respectively.