The distribution of dataset train and test are the differents, how to fix this?

I am new in data science and like some help to understand my problem. For instance, I have two signals non-stationary for the same condition (figure 1).

I acquisition them at different times(in the morning and in the afternoon), when applying the Kolmogorov-Smirnov test, the null hypothesis was rejected, I don’t understand why distribution is different if I no change any parameters in my system of acquisition.

This is the main trouble in my analysis because of this no get model any algorithm of machine learning to classification (overfitting).

I read something about the Covariate Shift (*I saw this post), and Kullback_Leiber Importance Estimation Procedure, but I don’t know iff will really work out.

*’Different Test Set and Training Set Distribution‘