"Dry gets drier, wet gets wetter": A case study over the arid regions of central Asia

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 Nino/Southern Oscillation (ENSO).