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High-resolution meteorological data products are crucial for agrometeorological studies. Here, we study the accuracy of an important gridded dataset, the near-surface temperature dataset from the 5 km × 5 km resolution China dataset of meteorological forcing for land surface modeling (published by the Beijing Normal University). Using both the gridded dataset and the observed temperature data from 590 meteorological stations, we calculate nine universal meteorological indices (mean, maximum, and minimum temperatures of daily, monthly, and annual data) and five agricultural thermal indices (first frost day, last frost day, frost-free period, and ≥0 °C and ≥10 °C active accumulated temperature, i.e., AAT0 and AAT10) of the 11 temperature zones over mainland China. Then, for each meteorological index, we calculate the root mean square errors (RMSEs), correlation coefficient and climate trend rates of the two datasets. The results show that the RMSEs of these indices are usually lower in the north subtropical, mid-subtropical, south subtropical, marginal tropical and mid-tropical zones than in the plateau subfrigid, plateau temperate, and plateau subtropical mountains zones. Over mainland China, the AAT0, AAT10, and mean and maximum temperatures calculated from the gridded data show the same climate trends with those derived from the observed data, while the minimum temperature and its derivations (first frost day, last frost day, and frost-free period) show the opposite trends in many areas. Thus, the mean and maximum temperature data derived from the gridded dataset are applicable for studies in most parts of China, but caution should be taken when using the minimum temperature data. [ABSTRACT FROM AUTHOR]

Crop simulation models play an essential role in evaluating irrigation management strategies for improving agricultural water use in crop production. In this study, the AquaCrop model was used to calibrate and validate the grain yield and biomass of 11 cultivars (9 winter wheat and 2 summer maize) under full and water-saving irrigation practices. The model results were verified with the available data from the published literature. Under the full irrigation practice, widely used by farmers in the region for winter wheat, the Normalized Root Mean Square Error (NRMSE) was found to be between 2.00–9.90% for grain yield, 2.40–10.85% for biomass, and 0.42–19.61% for water productivity. Under water-saving irrigation for winter wheat, the NRMSE range was found to be between 5.80–16.00% for grain yield, 3.70–17.30% for biomass, and 3.80–13.79% for water productivity. For summer maize, the NRMSE was 5.95%, 6.08%, and 16.41% under full irrigation, and 9.51%, 8.41%, and 3.60% under water-saving irrigation for grain yield, biomass, and water productivity, respectively. For winter wheat and summer maize, the simulation accuracy of grain yield was high under full irrigation, with percent deviations under ± 11.00%. In this study, the simulation accuracy for winter wheat was low under water-saving irrigation, while the model simulated the yield and biomass for summer maize to acceptable accuracy. The model can be reliably used as a tool to simulate grain yield and biomass across the NCP region for winter wheat and summer maize. However, the limitations of the model must be considered when simulating winter wheat under water-stressed conditions. • AquaCrop is a crop growth model to simulate yield response to water. • The model simulation accuracy was high under full irrigation for wheat and maize. • For water-saving irrigation, model accuracy was high for maize compared to wheat. • The model can be reliably used as a tool to simulate yield and biomass across NCP. [ABSTRACT FROM AUTHOR]