## 1 Introduction  
Since the groundbreaking research by [PERSON] and [PERSON] (1977) [1], followed by [PERSON] and [PERSON] (1991) [2] and [PERSON] (1992) [3], physics-based earthquake simulators have increasingly captivated the seismological community as a valuable tool for formulating and validating hypotheses about the earthquake process and interpreting earthquake data. These simulators can reproduce essential statistical relationships such as magnitude-frequency distributions, temporal patterns like the Omori law, and characteristics of earthquake clustering. By overcoming the inherent limitations of real-world observations such as completeness, homogeneity, and duration, earthquake simulators facilitate the assessment of diverse seismogenic process models and utilizing renewal models. Contributions from [PERSON] (1996 [4], 2000 [5], and 2012 [6]), [PERSON] (2012) [7], as well as [PERSON] et al. (2017) [8] and [PERSON] et al. (2018) [9], have further shown the capacity of their respective algorithms. While acknowledging that even the most sophisticated earthquake simulators are simplified models of the intricate reality of the earthquake process and that not all findings derived from such models can be exhaustively validated against real earthquakes, there is a consensus on their efficacy in enhancing the overall testing protocols for earthquake forecasting. This sentiment is shared by experts such as [PERSON] et al. (2015) [10], [PERSON] et al. (2017) [11], [PERSON] (2015 [12], 2019 [13]), and [PERSON] et al. (2022) [14]. We are interested in testing the capacity of the physics-based earthquake simulator developed in the last decade inside our research team ([PERSON] et al., 2022 [15], and references therein) to improve our comprehension of the seismogenic process. With this aim, we applied a previously tested version of the earthquake simulator to two well-known and completely different seismogenic structures, namely the Northern and Central Apennines, Italy, fault systems (since now referred to as CNAFS), and the Nankai megathrust, Japan, fault system (since now referred to as NMTFS). The earthquake simulation algorithm applied in our study is mainly based on simple physical principles underlying the elastic-rebound theory introduced by [PERSON] (1910) [16], and the theory of the elasticity for the computation of the Coulomb stress transfer from a ruptured fault patch to any other surrounding potential source ([PERSON] 2007 [17]). No use is made of non-linear physical ingredients such as the Dieterich Rate and State model or viscosity and fluid flow effects, based on the Occam principle that \"the simpler the better\", as shown by the realistic seismic patterns produced by our model. The simulator algorithm assumes a constant stress drop at each rupture of single patches and includes a set of (mainly two) free parameters that can be subjectively chosen by the user to control a rupture growth and better represent the real observations in a suitable way. In particular, [PERSON] and [PERSON] (2020) [18] gave detailed examples of the way these parameters affect the magnitude distribution of the synthetic catalogues and how the \"characteristic earthquake\" effect enhances the production of events rupturing a substantial fraction of entire fault sources rather than the background seismicity, which follows the Gutenberg-Richter law more closely. In the present study, we adopted the same set of parameters as in [PERSON] et al. (2022b) [15]. As already stated, in this work, we compared the seismogenic process in the following two completely different geodynamic contexts: the main crustal sources in Central Italy with the large sources of the subduction interface in Southwestern Japan. Reliable historical earthquakes are available for the distinct 43 seismogenic fault systems straddling the Northern and Central Apennines and for the 700 km long Japanese fault system (Figure 1).