Handling nonstationarity in the changepoint distribution / controlling future trend variance

Hi,
I really love the package, thank you very much for such an awesome work!
According to https://github.com/facebookincubator/prophet/blob/master/R/R/prophet.R#L877 the forecast change point follow a random uniform function which goes accordingly the paper:
> The assumption that the trend will continue to change with the same frequency and magnitude as it has in the history is fairly strong, so we do not expect the uncertainty intervals to have exact coverage.

One could imagine a prediction on data where the change point follow a non uniform distribution (i.e. new product appears following an exponential distribution). Even if the historical change point were manually entered the forecast would not follow the same distribution.
Is that a fair assumption? If yes, is there a way to account for that?
Another question that I have is concerning the rate adjustments in the forecast.
As far as I understand (from https://github.com/facebookincubator/prophet/blob/master/R/R/prophet.R#L887) the rate of change is assumed to have a shared lambda following a maximum likelihood from historical data.
However, aren't we possibly missing a trend in it?
One could imagine a model where the rate of adjustment keep getting smaller and smaller through time and even if lambda might be low, you might be missing the "trend".
Again, is this a fair assumption? and if yes, is there a way to account for that?