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• Drought propagation characteristics are analyzed over Central Asia. • BMA is used to build the relationship between drought propagation and factors. • The effects of factors on drought propagation are quantified by ANOVA. • Temperature is the most important factor on both propagation time and relationship. • The interactive effects of factors on drought propagation are significant. Drought poses a significant threat to agricultural sector, and meteorological drought (MD) is the main driver and origin of agricultural drought (AD). The process is susceptible to multiple potential factors, whereas there is a challenge in quantifying the effects of factors. This study develops a novel method named as BANOVA by combining Bayesian Model Averaging (BMA) with analysis of variance (ANOVA), and is tested in Central Asia which is an agricultural dominant area. Standardized Precipitation Index and Standardized Soil Moisture Index are calculated to characterize MD and AD from 1982 to 2014. Precipitation and soil moisture are provided by Climatic Research Unit Gridded Time Series and Global Land Data Assimilation System. The maximum Pearson correlation coefficients (MPCC) and drought propagation time (DPT) are selected to depict the process of drought propagation. The results suggest that BMA can effectively integrate the results of each machine learning model and reduce the uncertainty of model structure. Several findings can be summarized: (1) there is a stable relationship between AD and MD that MPCC are significant (p < 0.01) for all grids, and the average DPT is 2.8 months; (2) eight factors are selected for drought propagation simulation, and temperature plays the dominant role in both MPCC and DPT with the contribution of 86.2 % and 35.0 %; (3) the interactive effects of factors on drought propagation characteristics are significant, such as temperature and precipitation with the contribution of 13.2 % on drought propagation time. This study highlights the importance of temperature in drought propagation. Under the context of global warming, the propagation time and relationship between MD and AD will become shorter and closer, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • An integrated SDBC-BMA is firstly proposed for daily precipitation projection. • SDBC-BMA is applied to the Amu Darya River basin, Central Asia. • SDBC is robust compared with other statistical downscaling methods. • The results of SDBC-BMA are more accurate with lower uncertainty. • Both mean precipitation and extremes are expected to increase in the 21th century. Statistical downscaling of daily precipitation is important for assessing the impact of climate change. However, some issues (e.g., the underestimation of extremes) limit statistical downscaling methods to project daily precipitation accurately. This study develops an integrated hybrid statistical downscaling model combined with bias correction and Bayesian model averaging (named as SDBC-BMA) for improving the ability of downscaling methods. The performance of SDBC is compared with regression based statistical downscaling methods (RSDM) and hybrid statistical downscaling methods (HSDM) based on different performance metrics and extreme indices. SDBC-BMA is applied to the Amu Darya River Basin (ADRB) to demonstrate its feasibility and capability, where three GCMs and two CMIP6 scenarios are considered. Several findings can be summarized: (1) compared to RSDM and HSDM, SDBC is more robust with a higher computation ability of daily precipitation and a lower mean bias (i.e., +0.10 mm/day); (2) compared with the simple model average, the biases of extreme indices and mean daily precipitation obtained from SDBC-BMA could decrease by 49 % and 88 %, respectively; (3) the mean precipitation values would increase by 56 ± 41 mm/year (under SSP245) and 126 ± 55 mm/year (under SSP585) during 2076–2100, higher than mean precipitation in the historical period (1979–2005); (4) extreme indices under multi-GCMs (except for consecutive wet days) would increase by 20 % (under SSP245) and 22 % (under SSP585), implying that extreme events in ADRB are more frequent in the 21st century. The increasing risk of precipitation extremes would exacerbate threats to agricultural and ecological security. [ABSTRACT FROM AUTHOR]