Yes. I wrote this.
I’m no meterologist. But we all naturally expect the weather to act differently depending on the time of day, and the time of year.
The sine and cos signals I pass to the model are just a convenient and smooth way to tell it the time of day and time of year.
But even more simply, we also expect there to be some periodicity at 1/day and 1/year (the sun is important). A linear model with just those two sine and cos features will be able to account for that periodicity.
One way to think of the FFT is as the linear model weights for the sine and cos across every frequency. In this special case (orthonormal functions) the weights are the portion of the variance contained in the signal at each frequency.
You could use any sin/cos frequencies as input, but those two frequencies are good choices since that’s where a lot of the variance is.