With EUV lithography on the ITRS roadmap for sub-2X half-pitch patterning, it has become increasingly essential to ramp up efforts in being able to manufacture defect-free reticles or at least ones with minimal defects initially. For this purpose, much of the focus in recent years has been in finding ways to adequately detect, characterize, and reduce defects on both EUV blanks and patterned masks. For detection purposes, the current high-resolution DUV or e-beam inspection platforms are being extended to inspect EUV blanks and patterned masks but being non-actinic, make it very challenging to assess the real impact of the detected defects on EUV plane. Even with the realization of the EUV beta AIMS™ aerial-image based metrology in 2014-2015, the exact nature of each critical defect needs to be determined in order to be able to come up with an appropriate repair strategy. In this paper, we demonstrate the application of computational techniques to non-actinic supplemental metrology data collected on EUV mask defects to effectively determine the nature and also predict printability of these defects. The fundamental EUV simulation engine used in this approach is the EUV Defect Printability Simulator (DPS), which uses simulation and modeling methods designed specifically for the individual EUV mask components, and achieves runtimes several orders of magnitude faster than rigorous FDTD and RCWA methods while maintaining adequate accuracy. The EUV DPS simulator is then coupled with supplemental inspection and metrology measurements of real defects to effectively predict wafer printability of these defects. Several sources of such supplementary data are explored here, and may sometimes be dependent on the actual nature of defect. These sources include AFM height-profile data, SEM top-down images, and 193nm high-NA inspection images of single or multiple focus plane capture. From each of these supplemental data sources, the mask pattern and defect information is first extracted or recovered, and then forward-simulated in DPS to generate EUV aerial images subsequently analyzed for wafer printability. Each of the data sources have their strengths and limitations vis-à-vis use in a production pilot line. We exploit a mix and- match approach to effectively filter down to the defects that really matter. The 193nm inspection image data are readily available and although the pixel-sizes are somewhat coarse compared with the mask pattern widths, computationally predicting EUV printability off these images provides a quick filter of the obvious false and nuisance defects. SEM images on the other hand provide a much better two-dimensional top-down resolution of the patterns and hence work well for full-height excess or missing absorber defects but not so well for three-dimensional defects such as pits and bumps in the EUV multilayer or foreign material defects such as contamination. AFM height profile measurements generally provide the best available resolution on three-dimensional defects and thereby are well-suited for further simulations to EUV, however, AFM tip and image stability, and data acquisition time need to be comprehended. Computationally exploiting these supplemental defect inspection and metrology data in this mix-and-match approach effectively filters defects down to those that really matter on printed wafer. We see this approach as being vital to getting comprehensive defect learnings during the EUV pilot phase implementation and delivering well-characterized EUV masks to the wafer fab at substantially lower cost-of-ownership.