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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). Study area and its topography, including the major rivers: Syr Darya and Amu Darya, the major mountains: the Tianshan Mountain, Kunlun Mountain and Altai Mountains. [ABSTRACT FROM AUTHOR]

The accuracies of gridded precipitation data sets are important for regional climate studies and hydrological models. In this study, the performances of Global Precipitation Climatology Centre (GPCC) V7, Climatic Research Unit (CRU) TS 3.22 and Willmott and Matsuura (WM) precipitation data sets were examined over central Asia by comparing them against observed precipitation records (OBS) from 586 meteorological stations during 1901–2010. The results show that all the three gridded data sets underestimated the observed precipitation at annual and monthly scales, especially in mountainous areas. Both GPCC and WM underestimated seasonal precipitation, especially for spring precipitation. Among the three gridded data sets, GPCC had the highest correlation and lowest bias compared with CRU and WM when against the OBS. WM had a higher correlation than that of CRU, and its bias was larger than that of CRU. In terms of the drought and heavy rainfall events, CRU had the best performance in capturing drought events, and GPCC was best at representing heavy rainfall events. These differences in the performances between the three gridded data sets were primarily induced by their different interpolation methods and the numbers of available meteorological stations used in the interpolations of the three gridded data sets. Therefore, compared to the other two data sets, GPCC is more suitable for studies of long‐term precipitation variations over central Asia. [ABSTRACT FROM AUTHOR]

ABSTRACT This study examines the temporal variations and spatial distributions of annual precipitation over Central Asia during the periods of 1901-2013, 1951-2013, and 1979-2013 using the latest version of Global Precipitation Climatology Centre (GPCC) full data reanalysis version 7 ( GPCC V7) data set. The linear trend and multiperiods of the precipitation over the entire region and plain and mountainous area separately are analysed by linear least square method and ensemble empirical mode decomposition method. An overall increasing trend [0.66 mm (10 years)−1] is found for the entire region during 1901-2013, which is smaller than that of 1951-2013. The regional annual precipitation exhibits multi-decadal variations, with a sharp decline during 1901-1944, followed by an increase until 1980s, and a fluctuation thereafter. During 1979-2013, the mountainous area shows a greater increasing trend than the entire region. Furthermore, the regional annual precipitation has exhibited high-frequency variations with 3-year and 6-year quasiperiods and a low-frequency variation with 28-year quasiperiods. In terms of the spatial distribution, increasing trend in the annual precipitation is found in Xinjiang and decreasing trends appear over the five countries of Central Asia during 1951-2013. Empirical orthogonal function results show that the mountainous area is the large variability centre of the annual precipitation. The dominant mode of interannual variability in Central Asia annual precipitation is related to El Niño-Southern Oscillation, which explains about 17% of the interannual variance during 1951-2013. The results of this study describe the long-term variation in the annual precipitation over Central Asia as well as its relationship with some key climate indices in great detail, which will benefit the understanding and the prediction of the climate variations in this region. [ABSTRACT FROM AUTHOR]

The natural ecosystem in Central Asia is sensitive and vulnerable to the arid and semiarid climate variations, especially the climate extreme events. However, the climate extreme events in this area are still unclear. Therefore, this study analyzed the climate variability in the temperature and precipitation extreme events in an alpine grassland (Bayanbuluk) of Central Asia based on the daily minimum temperature, daily maximum temperature, and daily precipitation from 1958 to 2012. Statistically significant ( p < 0.01) increasing trends were found in the minimum temperature, maximum temperature at annual, and seasonal time scales except the winter maximum temperature. In the seasonal changes, the winter temperature had the largest contribution to the annual warming. Further, there appeared increasing trends for the warm nights and the warm days and decreasing trends for the cool nights and the cool days at a 99 % confidence level. These trends directly resulted in an increasing trend for the growing season length (GSL) which could have positively influence on the vegetation productivity. For the precipitation, it displayed an increasing trend for the annual precipitation although it was not significant. And the summer precipitation had the same variations as the annual precipitation which indicated that the precipitation in summer made the biggest contribution to the annual precipitation than the other three seasons. The winter precipitation had a significant increasing trend (1.49 mm/10a) and a decreasing trend was found in spring. We also found that the precipitation of the very wet days mainly contributes to the annual precipitation with the trend of 4.5 mm/10a. The maximum 1-day precipitation and the heavy precipitation days only had slight increasing trend. A sharp decreasing trend was found before the early 1980s, and then becoming increase for the above three precipitation indexes. The climate experienced a warm-wet abrupt climate change in the 1980s. Further, this tendency may be continuous into the future. [ABSTRACT FROM AUTHOR]

The relationship between the large-scale circulation dynamics and regional precipitation regime in the Tibetan Plateau (TP) has so far not been well understood. In this study, we classify the circulation types using the self-organizing maps based on the daily field of 500 hPa geopotential height and link them to the precipitation climatology in the eastern and central TP. By virtue of an objective determining method, 18 circulation types are quantified. The results show that the large amount of precipitation in summer is closely related to the circulation types in which the enhanced and northward shifted subtropical high (SH) over the northwest Pacific and the obvious cyclconic circulation anomaly over the Bay of Bengal are helpful for the Indian summer monsoon and East Asian summer monsoon to take abundant low-latitude moisture to the eastern and southern TP. On the contrary, the dry winter in the central and eastern Tibet corresponds to the circulation types with divergence over the central and eastern TP and the water vapor transportations of East Asian winter monsoon and mid-latitude westerly are very weak. Some circulation types are associated with some well-known circulation patterns/monsoons influencing the TP (e.g. East Atlantic Pattern, El Niño Southern Oscillation, Indian Summer Monsoon and the mid-latitude westerly), and exhibit an overall good potential for explaining the variability of regional seasonal precipitation. Moreover, the climate shift signals in the late 1970s over the eastern Pacific/North Pacific Oceans could also be reflected by both the variability of some circulation types and their correspondingly composite precipitations. This study extends our understandings for the large-scale atmospheric dynamics and their linkages with regional precipitation and is beneficial for the climate change projection and related adaptation activities in the highest and largest plateau in the world. [ABSTRACT FROM AUTHOR]

The resolution mismatch between GCMs and in-situ gauge observations is an issue that has to be addressed for assessments and projections of precipitation extremes. The impacts of using different strategies to address this issue on GCM assessments and projections are evaluated in this study. The differences of precipitation extremes derived from GCMs at the original gridded resolutions and site-scale observations can be mostly explained by resolution mismatch. As the spatial and temporal "discontinuous" nature of precipitation, consecutive dry days (precipitation intensity) estimated from GCM data over a grid are likely to be shorter (smaller) than in-situ observations. By interpolating GCMs and observations to a common resolution, areal differences are moderately reduced, but spatial correlations between GCMs and observations may not be necessarily improved. By statistically downscaling the GCM-derived precipitation extremes, the indices agree better with the in-situ observations substantially. Using interpolation or downscaling to resolve resolution mismatch in GCMs may result in contradictory projected changes in extremes. Downscaled precipitation extremes generally change in greater magnitude than interpolated extremes in the projections. • Resolution mismatch explains the differences in precipitation extremes between the gridded GCMs and site-scale observations. • Interpolation partially reduces the differences, and downscaling largely improves the performance of GCM-based extremes. • Using interpolation or downscaling to resolve resolution mismatch may result in contradictory projected changes in extremes. [ABSTRACT FROM AUTHOR]