How can we make use of feature variables whose future values are fixed to predict target value?

Question

With regard to time series features in a regression ML model. Suppose, we are living in a space colony. The temperature there is accurately under control, so we will know the temperature next week. Now, I have a problem predicting ice cream sales next week. Feature values are past sales records and temp values.

In this case, I believe that the fixed temp next week will help raise the accuracy of the sales prediction but I cannot come up with how to use this temp. I should split training/validation datasets from past data with train_test_split() as always. But I do not know how to handle the fixed future values. Does somebody know how to?