Understanding and quantifying potential drivers of compound extremes: A complex networks based on multiscale entropy approach

The compound dry and hot event (CDHE) has been paying attention in recent decades due to its disastrous impacts on diverse sectors. The teleconnections between dry conditions and large-scale circulation patterns have been widely studied at different spatial and temporal scales. However, studies investigating the links between large-scale circulation patterns and CDHE using a multiscale approach is missing. Quantifying the external forcing of compound dry and hot extremes (CDHE) is tedious and demands in-depth understanding.  We introduce a novel method by integrating wavelets, entropy and complex networks to quantify the potential drivers linked with CDHE. Firstly, a standardized dry and hot Index (SDHI) is developed to model the combined effect of precipitation and temperature using a copula approach. Second, the SDHI and Sea Surface Temperature (SST) is decomposed using wavelets to comprehend multiscale dynamical processes across time scales. Next, entropy is employed to quantify the similarity between SDHI and SST across multiple timescales. The proposed method uses the wavelet energy distribution of CDHI at different time scales and compares it with the wavelet energy distribution of SST to quantify the similarity. From similarity, complex networks is constructed to bridge the links between CDHE and circulation patterns. To investigate the efficiency and reliability, the proposed method is explored to improve the understanding and quantify the potential drivers of CDHE at a regional scale during the summer monsoon in India.  The results show that an integrated approach combining wavelets, entropy and complex networks offers a fresh perspective in analyzing the teleconnections between the compound extremes and large scale circulation patterns.