Large earthquakes like in Wenchuan in 2008, MW 7.9, Sichuan, China, provide opportunity for earthquake early warning (EEW) as many heavily shaken areas are far (~50 km) from the epicenter and warning time could be long enough (≥ 5 s) to take effective preventative action. On the other hand, earthquakes with magnitudes larger than ~M 6.5 are challenging for EEW since source dimensions need to be defined in order to adequately estimate shaking. The Finite-Fault Rupture Detector (FinDer) is an approach to identify fault rupture extents from real-time strong motion and/or broadband records. In this study, we playback local and regional on-scale strong motion waveforms recorded during the 2008 MW 7.9 Wenchuan, 2013 MW 6.6 Lushan, and 2017 MW 6.5 Jiuzhaigou earthquakes to study the performance of FinDer for the current layout of the China Strong Motion Network. Overall, the FinDer line-source models agree well with the observed spatial distribution of aftershocks and fault models determined from waveform inversion. However, since FinDer models are constructed to characterize seismic ground motions (as needed for EEW) instead of source parameters, the rupture length can be overestimated for events radiating high levels of high-frequency motions, as is the case in the Lushan earthquake. If the set of strong motion data used had been available in real-time, 50% to 80% of sites experiencing shaking of intensity MMI IV-VII (light to very strong) and 30% experiencing VIII-IX (severe to violent) could have been issued a warning with 10 s and 5 s, respectively, before the arrival of the destructive S-wave. We also show that loss estimates after devastating earthquakes based on the FinDer line-source are more accurate compared to a point-source model. For the Wenchuan earthquake, for example, they predict a four to six times larger number of fatalities and injured, which is consistent with official reports. At the same time, these losses could be provided 1/2~3 hours faster than if based on more complex inversion rupture models.