Based on continuum power regression (CPR) method, a novel derivation of kernel partial least squares (named CPR-KPLS) regression is proposed for approximating arbitrary nonlinear functions. Kernel function is used to map the input variables(input space) into a Reproducing Kernel Hilbert Space (so called feature space), where a linear CPR-PLS is constructed based on the projection of explanatory variables to latent variables (components). The linear CPR-PLS in the high-dimensional feature space corresponds to a nonlinear CPR-KPLS in the original input space. This method offers a novel extension for kernel partial least squares regression(KPLS),and some numerical simulation results are presented to illustrate the feasibility of the proposed method.