  This work focuses on solving super-linear stochastic differential equations
(SDEs) involving different time scales numerically. Taking advantages of being
explicit and easily implementable, a multiscale truncated Euler-Maruyama scheme
is proposed for slow-fast SDEs with local Lipschitz coefficients. By virtue of
the averaging principle, the strong convergence of its numerical solutions to
the exact ones in pth moment is obtained. Furthermore, under mild conditions on
the coefficients, the corresponding strong error estimate is also provided.
Finally, two examples and some numerical simulations are given to verify the
theoretical results.
