The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ET_c) and effective precipitation (P_e) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and P_e by copula and the temporal dependence of the measured ETc or P_e by copula based on Markov process and simulated data by Monte Carlo. Based on the P_e and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more P_e and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years P_e and ETc data captured the same statistics and dependence characteristics of the measured data. When p (P_e) > 25% and p (ETc) < 62.5%, the P_e was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When P_e was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making.