The forecasting performance for daily reference evapotranspiration (ET₀) at six weather stations in Canada using the Penman–Monteith (P–M) model and statistically downscaled global climate model (GCM) projections was evaluated and quantified. The observational daily weather data for ET₀ calculations, including the daily air temperature (T_a), solar radiation (R_s), wind speed (U_w), and relative humidity (R_H), were collected. The GCM data obtained by the ESM2M model for the same variables, period, and stations were extracted and downscaled using the Quantiles–matching approach. The forecasting performance for the weather variables was quantified, and the results showed that the raw GCM data for R_s and T_a over Canada matched the observations very well, but the GCM performed relatively worse in forecasting U_w and R_H. The Quantiles–matching downscaling approach can significantly improve the forecast accuracy of the meteorological variables. The ET₀ calculated using the raw GCM or downscaled GCM data were then compared with those computed with the observational data. The results demonstrated that the daily ET₀ over Canada can be satisfactorily forecasted using the P–M model and statistically downscaled GCM projections.

使用Penman–Monteith（PM）模型和统计缩减的全球气候模型（GCM）预测，对加拿大六个气象站的每日参考蒸散量（ET ₀）的预报性能进行了评估和量化。用于观测每日天气数据ET ₀计算，包括日常空气温度（Ť_一个），太阳辐射（ř_小号），风速（Ú_瓦特），和相对湿度（ř _ħ），被收集。由ESM2M模型获得的相同变量，周期和测站的GCM数据使用分位数匹配方法进行了提取和缩减。对天气变量的预报性能进行了量化，结果表明加拿大的R _s和T _a的原始GCM数据与观测值非常吻合，但GCM在预测U _w和R _H方面表现相对较差。分位数匹配的降尺度方法可以显着提高气象变量的预测准确性。该ET ₀然后，将使用原始GCM或缩减后的GCM数据计算的结果与使用观测数据计算的结果进行比较。结果表明，使用PM模型和GCM预测的统计数据可以很好地预测加拿大的每日ET ₀。