  Regional climate models (RCMs) are essential tools for simulating and
studying regional climate variability and change. However, their high
computational cost limits the production of comprehensive ensembles of regional
climate projections covering multiple scenarios and driving Global Climate
Models (GCMs) across regions. RCM emulators based on deep learning models have
recently been introduced as a cost-effective and promising alternative that
requires only short RCM simulations to train the models. Therefore, evaluating
their transferability to different periods, scenarios, and GCMs becomes a
pivotal and complex task in which the inherent biases of both GCMs and RCMs
play a significant role. Here we focus on this problem by considering the two
different emulation approaches proposed in the literature (PP and MOS,
following the terminology introduced in this paper). In addition to standard
evaluation techniques, we expand the analysis with methods from the field of
eXplainable Artificial Intelligence (XAI), to assess the physical consistency
of the empirical links learnt by the models. We find that both approaches are
able to emulate certain climatological properties of RCMs for different periods
and scenarios (soft transferability), but the consistency of the emulation
functions differ between approaches. Whereas PP learns robust and physically
meaningful patterns, MOS results are GCM-dependent and lack physical
consistency in some cases. Both approaches face problems when transferring the
emulation function to other GCMs, due to the existence of GCM-dependent biases
(hard transferability). This limits their applicability to build ensembles of
regional climate projections. We conclude by giving some prospects for future
applications.

Atmospheric and Oceanic Physics;Machine Learning