When a solar cell is subjected to a negative voltage bias, it locally heats up due to the deposited electrical power. Therefore, every investigation of cell characteristics in the negative voltage regime faces the challenge that the measurement itself changes the state of the cell in a way that is difficult to quantify: On the one hand, the reverse breakdown is known to be strongly temperature dependent. On the other hand, negative voltages lead to metastable device changes which are also very sensitive to temperature. In the current study, we introduce a new approach to suppress this measurement-induced heating by inserting time delays between individual voltage pulses when measuring. As a sample system we use thin-film solar cells based on Cu(In,Ga)Se2 (CIGS) absorber layers. First we verify that with this approach the measurement-induced heating is largely reduced. This allows us to then analyse the impact of the heating on two characteristics of the cells: (i) the reverse breakdown behaviour and (ii) reverse-bias-induced metastable device changes. The results show that minimising the measurement-induced heating leads to a significant increase of the breakdown voltage and effectively slows down the metastable dynamics. Regarding the reverse breakdown, the fundamental tunneling mechanisms that are believed to drive the breakdown remain qualitatively unchanged, but the heating affects the quantitative values extracted for the associated energy barriers. Regarding the reverse-bias metastability, the experimental data reveal that there are two responsible mechanisms that react differently to the heating: Apart from a charge redistribution at the front interface due to the amphoteric (VSe–VCu) divacancy complex, the modification of a transport barrier is observed which might be caused by ion migration towards the back interface. The findings in this study demonstrate that local sample heating due to reverse-bias measurements can have a notable impact on device behaviour which needs to be kept in mind when developing models of the underlying physical processes.