Computational problems in simulating biomass thermochemical processes involve coupled processes that span several orders of magnitude in space and time. Computational difficulties arise from a multitude of governing equations, each typically applicable over a narrow range of spatiotemporal scales, thus making it necessary to represent the processes as the result of the interaction of multiple physics modules, termed here as multiscale/ multiphysics (MSMP) coupling. Predictive simulations for such processes require algorithms that efficiently integrate the underlying MSMP methods across scales to achieve prescribed accuracy and control computational cost. In addition, MSMP algorithms must scale to one hundred thousand processors or more to effectively harness new computational resources and accelerate scientific advances. In this chapter, we discuss state-of-the-art modeling of macro-scale phenomena in a biomass pyrolysis reactor along with details of shortcomings and prospects in improving predictability. We also introduce the various multiphysics modules needed to model thermochemical conversion at lower spatiotemporal scales. Furthermore, we illustrate the need for MSMP coupling for thermochemical processes in biomass and provide an overview of the wavelet-based coupling techniques we have developed recently. In particular, we provide details about the compound wavelet matrix (CWM) and the dynamic CWM (dCWM) methods and show that they are highly efficient in transferring information among multiphysics models across multiple temporal and spatial scales. The algorithmic gain is in addition to the parallel spatial scalability from traditional domain decomposition methods. The CWM algorithms are serial in time and limited by the smallest-system time-scales. To relax this algorithmic constraint, we have recently coupled time parallel (TP) algorithms to CWM, thus yielding a novel approach termed tpCWM. We present preliminary results from the tpCWM technique, indicating that we can accelerate time-to-solution by two to three orders of magnitude even on 20-processors. These improvements can potentially constitute a new paradigm for MSMP simulations. If such improvements in simulation capability can be generalized, the tpCWM approach can lead the way to predictive simulations of biomass thermochemical processes.