基于112个站点干旱期4月39年蒸发皿蒸发量和常规地面气象观测8要素数据，应用EOF和典型相关分析，深入论证各常规气象要素与气候蒸发量的相关性，分析并比较各要素对蒸发量场总方差的解释能力；同时应用线性回归分析作为验证，并探寻多气象要素对蒸发量模拟的最优要素组合。结果显示，从单要素影响角度分析，常规地面气象观测要素与蒸发量相关性的排列次序为：平均相对湿度＞平均气温＞平均地面温度＞日照时数＞平均风速＞平均水汽压＞气压＞降水量，这与蒸发的热力学和动力学理论解释相一致。回归分析验证了典型相关的主要结果；单个常规气象要素中平均相对湿度对气候蒸发量的模拟效果最好；基于平均相对湿度、平均气温、风速、日照时数和平均水汽压资料的前3个要素组合和全部5要素组合，分别是简便普通精度和高精度需求下常规气象要素推算模拟气候蒸发量的最优要素组合。本文加深了对气候蒸发量的相关认识，并对其模拟推算和空间分布量化有重要指导意义。

Based on pan-evaporation data of past years and other meteorological element data of conventional surface meteorological measurement from 112 weather stations, orthogonal expansion method and canonical correlation are used to investigate the correlativity between monthly evaporation and conventional meteorological elements and to analyze and compare the capacity for each conventional meteorological element to explain potential evaporation, while regression analysis is applied to validate the foregoing analysis results and to seek the optimalizing element combination for conventional meteorological elements to simulate evaporation. As it turned out, there were 6 conventional meteorological elements which had notable effects on evaporation. The degrees of correlation between conventional meteorological elements and evaporation were as follow: average relative humidity(H)＞average temperature(T)＞average land surface temperature(Dt)＞sunshine duration(S)＞average speed of air(W)＞average vapor pressure(Vp)＞average atmospheric pressure(P)＞amount of precipitation(R). Among these essentials, essential H could explain 63.5% population variance of field variables for evaporation. T and Dt also had greater explanatory ability (more than 31% population variance interpreted). The explanatory ability of S, W, Vp and P to population variance of evaporation was relatively approximate about 20%. And precipitation had little effects on evaporation. All these were in accordance with the explanation of thermodynamics and dynamics in water evaporation.
　　According to correlation analysis and regressive simulation, average relative humidity took precedence over other conventional meteorological elements in simulating evaporation. The combination of relative humidity attached speed of air was probably the best two-essential combinatory simulating evaporation. Based on average relative humidity, average temperature, average speed of air, sunshine duration and average vapor pressure, the first three-essential combination and five-essential combination were respectively the optimum combination simulating evaporation by conventional meteorological elements under simply-universal requirement and high-precision requirement. The three-essential combination generated an average relative error of fitting, 2.77%, in this simulation while the five-essential combination made the identical error equal to 1.96%.
　　In the paper, mutuality of spatial distribution for essentials is analyzed by orthogonal expansion method and canonical correlation at the same time as correlativity of their temporal changes is investigated by regressive simulation (stepwise regression included). Both methods have validated and replenished opposite party reciprocally. So demonstration by reasoning is all-around and systematical here. The research has deepened the understanding related to potential evaporation, and is meaningful for reckoning evaporation and quantizing its spatial distribution.