Eighteen global circulation models (GCMs) were evaluated to determine the potential impacts of future climate change on irrigated and rainfed maize yields using the FAO AquaCrop model on an inter-annual and decadal basis (2020 s until 2090 s). Prior to deemed fit for future simulations, AquaCrop model was subject to comprehensive calibration and validation using extensive field-measured long-term datasets. We observed declines in (decadal) rainfed maize yields, ranging from 2.2% (0.2 t/ha) to 17% (1.4 t/ha) and from 8.1% (0.6 t/ha) to 21.5% (1.7 t/ha) under Representative Concentration Pathways (RCPs) RCP 4.5 and RCP 8.5, respectively. The range of declines was lower for irrigated yields [3.7% (0.5 t/ha) to 6.7% (1.0 t/ha) and 4.3% (0.6 t/ha) to 15.6% (2.2 t/ha) under RCP 4.5 and RCP 8.5, respectively]. Near maximal yield declines were distributed uniformly across the century and almost all decades exhibited > 10% yield declines under at least one emission scenario. Both economic (grain yield) advantage associated with irrigation (difference in irrigated and rainfed yields) and yield stabilizing benefit of irrigation (difference in rainfed and irrigated yield variability) are projected to decrease significantly (p < 0.05) under RCP 8.5. Rainfed maize yield variability was 533% and 200% greater than irrigated yield variability under RCP 4.5 and RCP 8.5, respectively. For RCP 4.5, the long-term mean inter-GCM (2020–2099) standard deviation in rainfed yields (4.6 t/ha) was 460% greater than that in irrigated yields (0.8 t/ha), while for RCP 8.5, this difference was 271% (4.6 t/ha vs. 1.2 t/ha). T_max and T_min were able to explain more variability in irrigated than rainfed maize yields, the difference being 229% and 126%, respectively. Precipitation change explained 46% and 50% of the variability in rainfed yield change under RCP 4.5 and RCP 8.5, respectively, and was 100% and 733% greater than what was explained for irrigated yield variability. The research findings hold significance for water allocation considering how dynamics of grain yields vs. availability of irrigation may manifest in the future.

评估了 18 个全球环流模型 (GCM)，以确定未来气候变化对使用粮农组织 AquaCrop 模型在年际和年代际（2020 年代至 2090 年代）灌溉和雨养玉米产量的潜在影响。在被认为适合未来模拟之前，AquaCrop 模型需要使用广泛的现场测量长期数据集进行全面校准和验证。我们观察到（十年）雨养玉米产量下降，范围从 2.2%（0.2 吨/公顷）到 17%（1.4 吨/公顷）和从 8.1%（0.6 吨/公顷）到 21.5%（1.7 吨/公顷）。代表性浓度途径 (RCP) 分别为 RCP 4.5 和 RCP 8.5。在 RCP 4.5 和 RCP 下，灌溉产量的下降幅度较低 [3.7% (0.5 吨/公顷) 至 6.7% (1.0 吨/公顷) 和 4.3% (0.6 吨/公顷) 至 15.6% (2.2 吨/公顷) 8.5，分别]。近乎最大的产量下降在整个世纪中均匀分布，并且几乎所有几十年都在至少一种排放情景下表现出 > 10% 的产量下降。在 RCP 8.5 下，与灌溉相关的经济（粮食产量）优势（灌溉和雨育产量的差异）和灌溉的产量稳定效益（雨育和灌溉产量变异性的差异）预计将显着下降（p < 0.05）。在 RCP 4.5 和 RCP 8.5 下，雨养玉米产量变异性分别比灌溉产量变异性大 533% 和 200%。对于 RCP 4.5，雨养产量（4.6 吨/公顷）的长期平均 GCM 间标准差（4.6 吨/公顷）比灌溉产量（0.8 吨/公顷）高 460%，而对于 RCP 8.5，这差异为 271%（4.6 吨/公顷对 1.2 吨/公顷）。吨 在 RCP 8.5 下，与灌溉相关的经济（粮食产量）优势（灌溉和雨育产量的差异）和灌溉的产量稳定效益（雨育和灌溉产量变异性的差异）预计将显着下降（p < 0.05）。在 RCP 4.5 和 RCP 8.5 下，雨养玉米产量变异性分别比灌溉产量变异性大 533% 和 200%。对于 RCP 4.5，雨养产量（4.6 吨/公顷）的长期平均 GCM 间标准差（4.6 吨/公顷）比灌溉产量（0.8 吨/公顷）高 460%，而对于 RCP 8.5，这差异为 271%（4.6 吨/公顷对 1.2 吨/公顷）。吨 在 RCP 8.5 下，与灌溉相关的经济（粮食产量）优势（灌溉和雨育产量的差异）和灌溉的产量稳定效益（雨育和灌溉产量变异性的差异）预计将显着下降（p < 0.05）。在 RCP 4.5 和 RCP 8.5 下，雨养玉米产量变异性分别比灌溉产量变异性大 533% 和 200%。对于 RCP 4.5，雨养产量（4.6 吨/公顷）的长期平均 GCM 间标准差（4.6 吨/公顷）比灌溉产量（0.8 吨/公顷）高 460%，而对于 RCP 8.5，这差异为 271%（4.6 吨/公顷对 1.2 吨/公顷）。吨 雨养产量（4.6 吨/公顷）的长期平均 GCM 间标准差（4.6 吨/公顷）比灌溉产量（0.8 吨/公顷）高 460%，而对于 RCP 8.5，这一差异为 271% （4.6 吨/公顷对 1.2 吨/公顷）。吨 雨养产量（4.6 吨/公顷）的长期平均 GCM 间标准差（4.6 吨/公顷）比灌溉产量（0.8 吨/公顷）高 460%，而对于 RCP 8.5，这一差异为 271% （4.6 吨/公顷对 1.2 吨/公顷）。吨_max和 T _min能够解释灌溉比雨养玉米产量更多的变异性，差异分别为 229% 和 126%。降水变化分别解释了 RCP 4.5 和 RCP 8.5 下雨育产量变化的 46% 和 50%，比灌溉产量变化的解释大 100% 和 733%。考虑到粮食产量与灌溉可用性的动态在未来可能如何体现，研究结果对于水资源分配具有重要意义。