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The arid region of northwest China (ARNC) is one of the most sensitive areas to global warming. However, the performance of new Global Climate Models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating climate in this region, especially in the subregions, is not clear yet. Based on the temperature dataset from historical runs of CMIP6, this paper analyzed and evaluated the simulation ability of 29 GCMs in reproducing the annual mean temperature (tas), annual mean maximum temperature (tasmax) and annual mean minimum temperature (tasmin) in the ARNC and subregions from 1961 to 2014. The results show that (1) the correlation coefficients (CCs) between simulation and observation time series for the mean of two model ensembles (MME for equal-weight multi-model ensemble and PME for preferred-model ensemble) are generally better than those of 29 individual GCMs, with CCs ranging from 0.38 to 0.87 (p < 0.01). (2) All the models can simulate the significant warming trend of the three temperature elements in the study area well. However, the warming magnitude simulated by most of the models (41%) is smaller than the observations except for tasmax, which is also shown in the MME. (3) The spatial pattern of the three temperature elements can be better reflected by most models. Model simulation ability for the ARNC is better compared to that of the four subregions, with a spatial CC greater than 0.7 (p < 0.01). Among the subregions, the simulation performance of the north of Xinjiang for spatial pattern is superior to that of the other regions. (4) The preferred models for each subregion are various and should be treated differently when used. Overall, the PME outperforms both the MME and the individual models; it can not only simulate the linear trend accurately but also reduce the deviation effectively. [ABSTRACT FROM AUTHOR]

The mountains in northern Xinjiang of China were studied during the snowmelt season. Multi-source fusions of live data of the Chinese Land Data Assimilation System (CLDAS, 0.05° × 0.05°, hourly data) were used as real data, and the Central Meteorological Observatory guidance forecast (SCMOC, 0.05° × 0.05°, forecasting the following 10 days in 3 h intervals) was used as forecast data, both of which were issued by the China Meteorological Administration. The dynamic linear regression and the average filter correction algorithms were selected to revise the original forecast products for SCMOC. Based on the conventional temperature forecast information, we designed four temperature-rise prediction algorithms for essential factors affecting snowmelt. The temperature-rise prediction algorithms included the daily maximum temperature algorithm, daily temperature-rise-range algorithm, snowmelt temperature algorithm, and daily snowmelt duration algorithm. Four temperature-rise prediction values were calculated for each prediction product. The root–mean-squared error algorithm and temperature prediction accuracy algorithm were used to compare and test each prediction algorithm value from the time sequence and spatial distribution. Comprehensive tests showed that the forecast product revised by the average filter algorithm was superior to the revised dynamic linear regression algorithm as well as the original forecast product. Through these algorithms, the more suitable temperature-rise forecast value for each grid point in the study area could be obtained at different prediction times. The comprehensive and accurate temperature forecast value in the mountainous snowmelt season could provide an accurate theoretical basis for the effective prediction of runoff in snowmelt areas and the prevention of snowmelt flooding. [ABSTRACT FROM AUTHOR]