Accurate estimation of reference crop evapotranspiration (ET₀) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET₀ across China. Monthly meteorological data [including maximum air temperature (T_max), minimum air temperature (T_min), extra-terrestrial solar radiation (R_a), relative humidity (RH), global solar radiation (R_s), wind speed (U)] during 1966–2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R² = 0.9814, RMSE = 0.2143 mm d⁻¹). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, R_s was greater than R_a, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7%, respectively), indicating a problem of overfitting. Conversely, the GWO-KNEA model was the most stable model with the smallest increase in RMSE during the testing phase (4.4–15.7%) among all models. In summary, taking the model's estimation accuracy, stability, convergence rate, and climate environment into account, our results suggest that GWO-KNEA would be a suitable model for estimating ET₀ in different climate zones of China and the most practical input combination would be combination 2.