Groundwater plays a major role in agro-hydrological processes in the North China Plain (NCP). The NCP is facing a water deficit, due to a rapid decline in the water table because of the double cropping system. A two crop (maize and wheat) rotation is required to balance the food supply and demand, which leads to an imbalance between evapotranspiration (ET) and precipitation. Thus, there has been a decline of about 1.35 m yr⁻¹ of groundwater (Luancheng Agroecosystem Experimental Station (LAES), NCP) during the last 10 years. Lysimeter experiments were conducted under different irrigation treatments (flood, surface drip, and subsurface drip) to account for ET in the selection of a suitable irrigation method. Subsurface drip irrigation reduced ET by 26% compared to flood irrigation, and 15% compared to surface drip irrigation, with significant grain yield and biomass formation due to decreased evaporation losses. Grain yield, yield components, and above ground biomass were similar in subsurface drip and flood irrigation. However, these biomass parameters were lower with surface drip irrigation. Furthermore, subsurface drip irrigation increased the crop water productivity (24.95%) and irrigation water productivity (19.59%) compared to flood irrigation. The subsurface irrigated plants showed an increase in net photosynthesis (~10%), higher intrinsic water use efficiency (~36%), lower transpiration rate (~22%), and saved 80 mm of water compared to flood irrigation. Our findings indicate that subsurface drip irrigation can be adopted in the NCP to increase water use efficiency, optimize grain yield, and minimize water loss in order to address scarcity.

The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of the final yield prediction. In this study, the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model and multiple climate indices (e.g., climate suitability indices and extreme climate indices) were incorporated into SRM to predict the wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the random forest (RF) algorithm outperformed the prediction models using other regression algorithms. The prediction for the wheat yield at SM (the period from the start of grain filling to the milky stage) based on RF can obtain a higher accuracy (r = 0.86, RMSE = 683 kg ha⁻¹ and MAE = 498 kg ha⁻¹). With the progression of wheat growth, the performances of yield prediction models improved gradually. The prediction of yield at FS (the period from flowering to the start of grain filling) can achieve higher precision and a longer lead time, which can be viewed as the optimum period providing the decent performance of the yield prediction and about one month’s lead time. In addition, the precision of the predicted yield for the irrigated sites was higher than that for the rainfed sites. The APSIM-simulated AB had an importance of above 30% for the last three prediction events, including FIF event (the period from floral initiation to flowering), FS event (the period from flowering to the start of grain filling) and SM event (the period from the start of grain filling to the milky stage), which ranked first in the prediction model. The climate suitability indices, with a higher rank for every prediction event, played an important role in the prediction model. The winter wheat yield in the NCP was seriously affected by the low temperature events before flowering, the high temperature events after flowering and water stress. We hope that the prediction model can be used to develop adaptation strategies to mitigate the negative effects of climate change on crop productivity and provide the data support for food security.

Changes in climate extremes have become a hot issue in the research field of climate change recently. Many studies have reported that climate extremes have occurred more frequently and with increasing intensity in recent decades. In this study, thresholds of precipitation and drought extremes were determined by the cumulative distribution function, and their spatiotemporal changes over the past half-century in China were analyzed by relative change rate. The results show that: (1) precipitation extremes increased in all regions except North China, while increasing trends of drought extremes were detected in all regions except Northwest China and the Qing–Tibet Plateau; (2) the maximum change rates in frequency of precipitation extremes were found in Northwest China and the Qing–Tibet Plateau, with values of 16.13% and 8.12%, and the maximum change rates in frequency of drought extremes were in Southwest and Southeast China, whose increases in intensity of drought extremes were also the maximum; (3) variation in precipitation extremes showed a relatively mixed pattern with higher heterogeneity compared to that of drought extremes; and (4) changes in precipitation and drought extremes relate to mid-intensity, lower-intensity, and annual precipitation.