Crop yields are greatly affected by climate change, but the suitability of meteorological factors for crop production is not fully understood. To improve the accuracy of crop climate suitability evaluation, we propose a new crop climate suitability evaluation (CCSE) model, which consists of the climate suitability guarantee rate (CSG) model and the yield prediction (YP) model, both well validated in Liaoning Province, China. The results showed that the correlation between climate suitability index (CSI) and relative ideal meteorological yield per unit area ( \(\:\triangle\text{I}{\text{Y}}_\text{w}\) ) was well fitted (R2 = 0.97). As the climate guarantee rate increased, the most suitable temperature area moved toward the southern and central parts of the province and became connected with a high-suitability area centered in Fuxin in the northwest. By contrast, low-suitability areas showed a contracting trend; high-suitability and low-suitability areas of sunlight suitability first contracted and then expanded, and the most suitable precipitation area moved northward and expanded, while low-suitability areas appeared to contract and converge. We found that the current meteorological conditions reached the ideal meteorological yield level of 20% per unit area ( \(I{Y_w}20\%\) ), and it is predicted that by 2030, spring maize yield per unit area is projected to reach 7652 kg/hm2. When the guarantee rate is increased to 95%, unit yield could rise by 27.9%. This method improves our ability to predict climate suitability for spring maize, and the results provide a new concept for agricultural production to adapt to climate change.
