Deciphering the teleconnections of extreme temperature indices with local scale meteorology using wavelet coherence

This study investigates the teleconnections of Extreme Temperature Indices (ETIs) with local meteorological parameters in a multi-scale perspective. Firstly, wavelet analysis was conducted on nine extreme temperature indices (ETIs) and three local meteorological variables over six major cities in India during the period 1981–2021, with the associations was studied using Bivariate Wavelet Coherence (BWTC) and Multivariate Wavelet Coherence (MWTC) approaches. The variability of extreme temperature indices is examined with local meteorological variables like Relative Humidity (RH), Surface Pressure (PS) and Wind speed at 2 m (WS2M) of Delhi, Mumbai, Chennai, Kolkata, Kochi and Guwahati which were distinctly different in climatic conditions, population and geographical features. The study revealed that significant coherence (AWC >0.5) exists between selected extreme temperature indices and each local meteorological variable. Relative humidity is the most dominating local meteorological variable in cities Delhi and Guwahati, surface pressure in Chennai and Kochi, and wind speed in Mumbai and Kolkata. It was observed that relative humidity, surface pressure and wind speed are significant contributors to temperature indices at a scale of 8–16 months except percentile-based indices TX10p and TN90p in BWTC analysis. Multiple wavelet coherences suggest that the extremes can be better explained by combining two or more parameters and each city each city has a unique combination of local meteorological variables that together best described variances in the extreme climate indices.