Downsampling - how many extra samples to include
I want to downsample a finite section of an equidistant time-series 
  by doing an FFT and dropping the upper part of the spectrum. 


Why? This one of the least efficient way to down sampling.  See Why is it a bad idea to filter by zeroing out FFT bins?

In order to down-sample you need to low pass filter first. There are many different ways of building a low pass filter and almost all of them are better than zero-ing FFT bins. 


  What heuristics could lead me to choose a reasonably appropriate number of extra samples (probably by specifying an accuracy parameter)?


You do this by first specifying the relevant requirements for your application: signal to noise ratio, phase distortion, pass band ripple, stop band ripple, latency, CPU/memory constraints. Some of those may be very important, some of those may be irrelevant, but it's different for each application.  Then you design a filter that makes sure you meet your requirements.