This study evaluates General Circulation Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for their ability in simulating historical means and extremes of daily precipitation (P), and daily maximum (Tmax), and minimum temperature (Tmin). Models are evaluated against hybrid observations at 2255 sub-basins across Alberta, Canada using established statistical metrics for the 1983–2014 period. Three extreme indices including consecutive wet days (CWD), summer days (SD), and warm nights (WN) are defined based on the peak over the threshold approach and characterized by duration and frequency. The tail behaviour of extremes is evaluated using the Generalized Pareto Distribution. Regional evaluations are also conducted for four climate sub-regions across the study area. For both mean annual precipitation and mean annual daily temperature, most GCMs more accurately reproduce the observations in northern Alberta and follow a gradient toward the south having the poorest representation in the western mountainous area. Model simulations show statistically better performance in reproducing mean annual daily Tmax than Tmin, and in reproducing annual mean duration compared to the frequency of extreme indices across the province. The Kernel density curves of duration and frequency as simulated by GCMs show closer agreement to that of observations in the case of CWD. However, it is slightly (completely) overestimated (underestimated) by GCMs for warm nights (summer days). The tail behaviour of extremes indicates that GCMs may not incorporate some local processes such as the convective parameterization scheme in the simulation of daily precipitation. Model performances in each of the four sub-regions are quite similar to their performances at the provincial scale. Bias-corrected and downscaled GCM simulations using a hybrid approach show that the downscaled GCM simulations better represent the means and extremes of P characteristics compared to Tmax and Tmin. There is no clear indication of an improved tail behaviour of GPD based on downscaled simulations.

The assessment of drought characteristics often depends on drought indices, geographic location, hydro‐climatic condition, and timescale. In this study, we examined the spatiotemporal characteristics of drought events using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Multivariate Standardized Drought Index (MSDI). We developed a novel framework using hydro‐climatic variables from a high‐resolution process‐based hydrologic model to understand factors that alter drought indices at various timescales, and their impact on crop yields in a large agricultural region of western Canada. These indices were used to quantify droughts for each month of the year by examining 1–12‐month drought timescales in 2255 sub‐basins, simulated in 17 main river basins across Alberta, for 1981–2017. Temporal variations of the Standardized Yield Residuals Series (SYRS) of three major cereal crops (spring wheat, barley, and canola) were analysed for 1981–2017. Drought characteristics resulting from two indices varied due to differences in the input variables and timescales. The MSDI‐based results showed more frequent droughts during the fall and winter for shorter timescales, while the SPEI‐based results showed more during spring and summer. Comparing drought frequencies at the decadal scale, we found more droughts during 1996–2005 than during 1986–1995 and 2006–2015. The spatial evolution of drought events based on the MSDI showed more sub‐basins with increasing dryness during the study period than did results from the SPEI. The relationship between detrended drought indices and the SYRS varied depending on timescale, geographic location, and growth stage of crops. Overall, both indices performed similarly for agricultural impact assessment; however, the MSDI performed better early in the growing season for wheat and barley, indicating high crop production sensitivity to soil moisture deficiency.

The responses of regional hydrological variables to climate change are of prime concern for agricultural water resources planning and management. Therefore, the seasonal (April–September) and annual (January–December) evolution of precipitation, temperature, evapotranspiration (ET), soil moisture (SM), deep aquifer recharge (DA), and water yield (WYLD) was investigated using established statistical techniques for the historical, near and far future (1983–2007: His, 2010–2034: NF, 2040–2064: FF) in the agricultural region of Alberta, Canada. Previously calibrated and validated agro-hydrological models (Soil and Water Assessment Tool) were used to generate these variables. Future changes were investigated under two representative concentration pathways, i.e., RCP 2.6 and RCP 8.5, projected by nine global climate models (GCM). Results revealed that Alberta had become warmer and drier during the His period. The future projection showed an increase in precipitation, SM, DA, and WYLD, in turn, indicated more water resources. Precipitation and temperature were projected to increase between 1 to 7% and 1.21 to 2.32 °C, respectively. Seasonal precipitation showed a higher trend magnitude than that of annual precipitation. The temperature generally had an increasing trend in the future with a maximum in the southern Alberta. Monthly average ET was likely to increase and decrease in the rising and falling limbs of the bell-shaped curve with the peak in July. A comparison of water demand from two land use types (dominant land use and barley) during the His period showed that water deficit existed in July and August. The results of this study could help in understanding anticipated changes in hydrological variables and decision-making regarding the regional agricultural water resources management.