Crop yield in rainfed conditions is greatly influenced by meteorological parameters such as rainfall and evapotranspiration. As climate indices (AO, NAO, PNA, NINO-3.4, and AAO) are available for the future months, finding the relationship between crop yield with meteorological parameters and the relationship between meteorological parameters with climate indices can be very useful in predicting annual fluctuations in rainfed crop yield. In the current study, the association between rainfall, PET, and AI with barley and wheat rainfed detrended yields (1990–2016) and the relationship between AI and climate indices (AO, NAO, PNA, NINO-3.4, and AAO) were assessed for Chaharmahal and Bakhtiari province in the west of Iran. All associations were assessed at annual and seasonal (wet and dry seasons) scale considering both concurrent and lag correlations (1-year and 2-year lag). Our results showed a significant correlation between rainfall and AI with crop yields in all study locations except Kouhrang in both annual and wet season time scales. The results indicated that except Kouhrang, where the temperature is a limiting factor to crop yields, in all Chaharmahal and Bakhtiari areas, drought is one of the key factors of annual yield variability (significant correlation between AI and crop yields). The significant correlation between AI with NINO-3.4 and AAO also was observed in both annual and wet season time scales. It seems that NINO-3.4 and AAO indices can be useful to estimate grain yield of rainfed barley and wheat, predict drought occurrence, and take possible actions to counter such conditions.

雨养条件下的作物产量受降雨和蒸散等气象参数的影响很大。由于气候指数（AO、NAO、PNA、NINO-3.4 和 AAO）可用于未来几个月，因此找到作物产量与气象参数之间的关系以及气象参数与气候指数之间的关系对于预测年度波动非常有用在雨养作物产量方面。在当前的研究中，评估了降雨量、PET 和 AI 与大麦和小麦雨育趋势单产（1990-2016）之间的关联以及 AI 与气候指数（AO、NAO、PNA、NINO-3.4 和 AAO）之间的关系位于伊朗西部的恰哈马哈尔省和巴赫蒂亚里省。考虑到并发和滞后相关性（1 年和 2 年滞后），所有关联都在年度和季节性（雨季和旱季）尺度上进行评估。我们的结果表明，除 Kouhrang 外，所有研究地点的降雨量和 AI 与作物产量之间在年度和雨季时间尺度上均存在显着相关性。结果表明，除了温度是作物产量限制因素的 Kouhrang，在所有 Chaharmahal 和 Bakhtiari 地区，干旱是年产量变异的关键因素之一（AI 与作物产量之间存在显着相关性）。在年度和雨季时间尺度上也观察到 AI 与 NINO-3.4 和 AAO 之间的显着相关性。似乎 NINO-3.4 和 AAO 指数可用于估计雨育大麦和小麦的粮食产量，预测干旱发生，