Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China’s vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland region), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited the highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET (evapotranspiration) and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions.

Water use efficiency ($WUE$) is one of the most widely used indicators in agricultural water management. Although this indicator has obvious advantages, it is limited to measuring the relationship between crop yield and corresponding water use. In recent years, many researchers have noted that understanding the economic efficiency of water use ($EEWU$) could have great water-saving potential, while it has been poorly investigated with respect to China’s agricultural water management. This paper assesses $EEWU$ through a household farmer-level survey in the piedmont region of North China. First, $EEWU$ of crops are estimated based on agricultural water consumption (including irrigation water and effective precipitation) and profit (including gross profit and net profit); Second, the impact of monthly price changes in 2019 and annual price changes in 2014–2019 on $EEWU$ is analysed. Main conclusions are as follows: (1) $EEWU$ values of cash crops such as apple and cauliflower are much higher than those of grain crops such as wheat and maize; (2) For different crops, the median economic efficiency of irrigation water ($EE{W}_{i}U$) and total water ($EE{W}_{t}U$) range from 31.71 to 99.54 ¥/m³ and 11.31 to 44.05 ¥/m³, respectively; (3) The multi-year average $EE{W}_{i}U$ and $EE{W}_{t}U$ ranged from 4.75 to 63.99 ¥/m³ and from 2.67 to 31.71 ¥/m³, respectively. Economic efficiency of water use shows a slightly downward trend in the period of study, which would contradict the trend towards the use of more water-efficient technologies and shows an even larger margin of improvement in the domain of agricultural water efficiency. The results provide a powerful reference for the management of agricultural water use through economic leverage.

Climate change has amplified the frequency of extreme high-intensity precipitation events, altering groundwater recharge across diverse hydrogeological settings. Using observed precipitation, groundwater level (GL), hydrogeological drilling data, and the Theis equation, we quantified how precipitation affects groundwater in the Baiyangdian-Basin Plain. We found that GL declined in 80% of counties, with a milder decrease in piedmont regions. During rainy and irrigation periods, the cumulative rise and fall of GL accounted for 55.3% and 57.5% of the annual total, respectively. Short-duration intense precipitation dominates annual water gain in wet years and amplifies inter- and intra-annual GL fluctuations. When precipitation exceeds 600 mm/year (intensity >8 mm/day), both GL recovery and well flow rate decline rapidly, implying reduced groundwater recharge. In the piedmont, precipitation is strongly positively correlated with GL and well flow rate, indicating little impact of extreme precipitation on groundwater recharge. These findings prevent the overestimation of groundwater recharge from extreme precipitation.

Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China's vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions.