To characterize in detail the spatial variability of ET0 at nationwide scale and identify the homogenous regions, we applied REOF to analyze the most dominant spatial patterns. The aim of the EOF method is to find a relatively small number of independent variables conveying as much of the original information as possible without redundancy by decomposing a multivariate data set into uncorrelated linear combination of separate function of the original variables. However, the physical interpretability of the obtained patterns is sometimes a matter of controversy because of orthogonality in both space and time68. This limitation has brought about the development of the rotated empirical orthogonal function (REOF)69, which can cluster within each mode a small number of high valued variables and a large number of near-zero value variables through a rotation to simple structure. In a comparison study by Kim and Wu70, REOF was found to be better in dividing climatic patterns. In this paper, with maximizing the variance of the squared correlation between each rotated principal component (RPCs) and each variable, the varimax REOF method was chosen to give the simplest pattern description while explaining the maximum amount of variance.