  Non-Gaussianity indicates complex dynamics related to extreme events or
significant outliers. However, the correlation between non-Gaussianity and the
dynamics of heterogeneous environments in anomalous diffusion remains
uncertain. Inspired by a recent study by Alexandre et al. [Phys. Rev. Lett.
130, 077101 (2023)], we demonstrate that non-Gaussianity can be deciphered
through the spatiotemporal evolution of heterogeneity-dependent diffusivity.
Using diffusion experiments in a linear temperature field and Brownian dynamics
simulations, we found that short- and long-time non-Gaussianity can be
predicted based on diffusivity distribution. Non-Gaussianity variation is
determined by an effective P\'eclet number (a ratio of the varying rate of
diffusivity to the diffusivity of diffusivity), which clarifies whether the
tail distribution expands or contracts. The tail is more Gaussian than
exponential over long times, with exceptions significantly dependent on the
diffusivity distribution. Our findings shed light on heterogeneity mapping in
complex environments using non-Gaussian statistics.
