A time-varying Copula-based approach to quantify the effects of antecedent drought on hot extremes

Hot extremes may adversely impact human health and agricultural production. Owing to anthropogenic and climate changes, the close and dynamic interaction between drought and hot extremes in most areas of China need to be revisited from the perspective of nonstationarity. This study therefore proposes a time-varying Copulabased model to describe the nonstationary dependence structure of summer extreme temperature (SET) and antecedent soil moisture condition to quantify the dynamic risk of hot extremes conditioned on dry/wet condition. A general statistical inspection procedure which was composed of maximum likelihood (ML)-based estimation, nonstationary Goodness-of-fit (GOF) tests, the log likelihood ratio (LR) tests and minimum corrected Akaike information criterion-based selection was promoted to select the best-fitted nonstationary models efficiently. This study proposed a new approach to identify the soil moisture driving law over extreme temperature from the point view of tail monotonicity and nonstationary risk assessment. Owing to the LTI-RTD (left tail increasing and right tail decreasing) tail monotonicity for dependence structure of these two extremes derived from most areas, two kinds of the driving laws of soil moisture over SET were detected. Because of the spatiotemporal divergence of sensitivity index derived from tail monotonicity (SITM), we can conclude that the spatial and temporal heterogeneity of response degree of ET over the variations of antecedent dry/wet conditions is evident. Incorporation of nonstationarity and tail monotonicity helps identify the changes of driving mechanism (laws) between soil moisture and hot extremes.