The emergence of flash drought has attracted widespread attention due to its rapid onset. However, little is known about the recent evolution of flash droughts in terms of the speed of onset and the causes of such a rapid onset phase of flash droughts. Here, we present a comprehensive assessment of the onset development of flash droughts and the underlying mechanisms on a global scale. We find that 33.64−46.18% of flash droughts with 5-day onset of drying, and there is a significant increasing trend in the proportion of flash droughts with the 1-pentad onset time globally during the period 2000−2020. Flash droughts do not appear to be occurring more frequently in most global regions, just coming on faster. In addition, atmospheric aridity is likely to create a flash drought-prone environment, and the joint influence of soil moisture depletion and atmospheric aridity further accelerates the rapid onset of flash droughts.

The consecutive heat wave and heavy rainfall (CHWHR) events, defined as the occurrence of heat waves followed by heavy rainfall, can cause more damages than individual extremes. Nevertheless, this type of compound event has not been diagnosed systematically. Here we examine the occurrence of CHWHR events and underlying characteristics. We find 22% of land areas experienced statistically significant CHWHR events within 7 days in China during 1981–2005, with an average 26% of heat waves being followed by heavy rainfall (vs. 10% expected by chance). More importantly, the shorter and hotter heat waves are more likely to be followed by heavy rainfall than other heat waves. This phenomenon is associated with atmospheric convection and moisture convergence. In addition, climate projection shows the CHWHR events will occur more frequently and abruptly in China throughout the 21st century, which contribute to the increased compound risk of back‐to‐back heat waves and flash floods.

Drought and pluvial transitions have attracted widespread attention. However, the dynamic evolution and underlying mechanisms of drought and pluvial transitions remain poorly understood. Here, we demonstrate that there is a significant increase in the occurrence of drought and pluvial transitions (0.24−1.03% per year) globally during 1980−2020. We use convergent cross mapping to detect causal relationships between time series variables in the climate system and find that drought and pluvial transitions can be explained by an indirect transitive causal chain. Specifically, the soil moisture−latent heat flux−precipitation causal chain is likely to trigger the rainfall following the dryness in humid regions where enhanced evaporation increases the actual atmospheric moisture favoring the pluvial occurrence. By contrast, the soil moisture−moisture convergence−precipitation causal chain enhances the post-drought rainfall in arid regions due to atmospheric circulation dynamics. Our results indicate that land−atmosphere feedbacks play an important role in triggering the drought-pluvial shift in a changing climate.

The “dry gets drier, wet gets wetter” (DGDWGW) paradigm well describes the pattern of precipitation changes over the oceans. However, it has also been usually considered as a simplified pattern of regional changes in wet/dry under global warming, although GCMs mostly do not agree this pattern over land. To examine the validity of this paradigm over land and evaluate how usage of drought indices estimated from different hydrological variables affects detection of regional wet/dry trends, we take the arid regions of central Asia as a case study area and estimate the drying and wetting trends during the period of 1950–2015 based on multiple drought indices. These indices include the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the Palmer drought severity index (PDSI) and self‐calibrating PDSI (sc_PDSI) with both the Thornthwaite (th) and Penman–Monteith (pm) equations in PDSI calculation (namely, PDSI_th, PDSI_pm, sc_PDSI_th and sc_PDSI_pm). The results show that there is an overall agreement among the indices in terms of inter‐annual variation, especially for the PDSIs. All drought indices except SPI show a drying trend over the five states of central Asia (CAS5: including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan). The four PDSIs and SPEI reveal a wetting tendency over the northwestern China (NW; including Xinjiang Uygur Autonomous Region and Hexi Corridor). The contrasting trends between CAS5 and NW can also be revealed in soil moisture (SM) variations. The nonlinear wet and dry variations are dominated by the 3–7 years oscillations for the indices. Relationships between the six indices and climate variables show the major drought drivers have regional features: with mean temperature (TMP), precipitation total (PRE) and potential evapotranspiration (PET) for CAS5, and PRE and PET for NW. Finally, our analyses indicate that the dry and wet variations are strongly correlated with the El Niño/Southern Oscillation (ENSO).

In this study, different interpolation techniques in a geographical information system (GIS) environment are analysed and compared for estimating the spatial distribution of precipitation in the province of Ontario, Canada. A high‐resolution regional climate modelling system [Providing Regional Climates for Impacts Studies (PRECIS)] is used to simulate the present (1961–1990) and future (2071–2100) precipitation events for 12 meteorological stations over Ontario. The results verify that for the present case PRECIS simulates well the precipitation events when compared with observed data. The future precipitation events can be projected after the validation of PRECIS. Six interpolation methods are then used to generate spatial distribution of precipitation based on the projections of future precipitation of 12 meteorological stations; they include inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), radial basis functions (RBF), ordinary kriging (OK), and universal kriging (UK). Cross‐validation is applied to evaluate the accuracy of interpolation methods in terms of the root mean square error (RMSE). The results indicate that LPI is the optimal method with the least RMSE for interpolating the PRECIS precipitation. LPI is then used to analyse spatial variations of the average annual precipitation for the period of 2071–2100 over Ontario.