Both moist heatwaves (HWs) and heavy precipitation events (HP) have increased in both frequency and magnitude over China in recent decades. However, the relationship between HW and HP and changes in the lagged coincidence of events (i.e., the occurrence of an HP event several days after an HW event, noted HWHP) remain unknown. We show here that HWHP events account for nearly one-third of HP events over China in summer, with high values in North China, Northeast China, and the East arid zone. HWHP events assessed using the heat index and the wet-bulb temperature methods increased by 45.25 and 23.97% from 1961 to 2019, respectively. These concurrent HWHP events tend to be spatially clustered, and the areas affected simultaneously have grown significantly. The increase in HW is the major driver of these changes in HWHP events, except in the western arid zone and North China. Our findings provide an understanding of the spatiotemporal changes in HWHP events over China and their implications for disaster mitigation.

<p>High temperatures and droughts pose a great threat to the human health, social economy and ecosystems. A large number of previous studies have focused on meteorological hot-dry events (based on temperature and precipitation), but there is a lack of comprehensive studies about hydrological hot-dry events (based on temperature and runoff). Here, using the ensemble empirical mode decomposition method and Copula function, we assess spatio-temporal evolution of global compound hot-dry events from temperature and runoff, and quantify their drivers based on monthly temperature and runoff data during 1902-2019. We find there is a significant warming at an unprecedented pace over the past 118 years, especially in the mid-latitudes of the Northern Hemisphere. However, changes in accumulated trends in precipitation and runoff show complex patterns globally. Probabilities of meteorological and hydrological hot-dry events both have been increasing significantly, but hydrological events are more likely to occur with higher spatial homogeneity, wider coverage and more severe damage. To analyze its underlying driving mechanism, we estimate quantitatively the contribution of high temperature, low runoff and the dependence between high temperature and low runoff to the compound event. High temperature plays a dominant role in the driving mechanism. In several regions, such as Australia, Europe and South America, hot-dry events could be considered as a potential hazard caused by increasing temperatures. Runoff deficit and dependence between the two, together with high temperature, exacerbate the occurrence of compound hot-dry events. Our findings provide a promising direction to predict joint probability of hot-dry events. Hydrological hot-dry events have seldom been considered, so far, in strategic policy formulation and risk assessment. Our results offer a powerful tool to improve planning and strategies to adapt to climate change.</p>

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Non-stationarity of extreme climate events has been reported worldwide in recent decades, and traditional stationary analysis methods are no longer sufficient to properly reveal the occurrence probability of climate extremes. Based on the 0.25°C × 0.25°C gridded precipitation data (i.e., CN05.1), stationary and non-stationary models of generalized extreme value (GEV) and generalized Pareto (GP) distributions are adopted to estimate the occurrence probability of extreme precipitation over China during 1961–2018. Low-frequency oscillation (LFO) indices, such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO), are included as time-varying covariates in the non-stationary GEV and GP models. Results illustrate that the occurrence probability of extreme precipitation estimated from the stationary GEV and GP distributions shows a significant increasing trend in northwestern and southeastern China, and the opposite trend in southwestern, central, and northeastern China. In comparison with stationary model, the fitness of extreme precipitation series is improved for both the GEV and GP distributions if these LFO indices are used as time-varying covariates. Positive ENSO, IOD and PDO tend to cause negative anomalies in the occurrence probability of extreme precipitation in northeastern China and Tibet Plateau, and positive anomalies in southern China. Positive NAO and SAM phases mainly tend to cause positive anomalies in southern China. The circulation patterns of extreme precipitation anomalies associated with these LFO indices are discussed from aspects of precipitable water, vertical integrated moisture transport, 500-hPa geopotential height and 850-hPa wind field.