I wouldn't use the term "baseline" when discussing the mean value of the errts time series.

The linear regression step of a time series basically asserts that a time series can be written as the sum of 1 or more provided components, each with a to-be-estimated coefficient (or beta weight); finding those coefficients is the point of the regression step. The sum of each component times its coefficient is the estimator for the original time series, and the difference between the original time series and its estimator is the residuals. Thus, the residuals are the leftover stuff when all the components have been fit for.

Among the common components explicitly included in a model is typically a constant time series, whose coefficient is the mean value of the time series. That (or that plus other low frequency/polynomial terms) is generally referred to as the baseline. Other components in FMRI often include motion regressors and perhaps censoring components. In resting state FMRI, you provide a list of regressors that are essentially all regressors of no interest---things that might be important to account for in the measured signal but which are not directly related to neuronal firing---and then one analyses what is left after those are regressed out as (hopefully) containing mostly neuronal signal of interest. Spoiler alert: there is surely more than just neuronal signal in the residuals; but that is the best estimate for it, so that gets analyzed.

The values in the errts do reflect BOLD % signal change. I might just state it as "BOLD % signal change." I guess you could state it as "0.34% BOLD signal change from estimated baseline", but I would not state "0.34% BOLD signal change from errts baseline of 0". Maybe that is splitting hairs, but the latter seems different.

--pt