Task clustering has proven to be an effective method to reduce execution overhead and to improve the computational granularity of scientific workflow tasks executing on distributed resources. However, a job composed of multiple tasks may have a higher risk of suffering from failures than a single task job. In this paper, we conduct a theoretical analysis of the impact of transient failures on the runtime performance of scientific workflow executions. We propose a general task failure modeling framework that uses a Maximum Likelihood estimation-based parameter estimation process to model workflow performance. We further propose 3 fault-tolerant clustering strategies to improve the runtime performance of workflow executions in faulty execution environments. Experimental results show that failures can have significant impact on executions where task clustering policies are not fault-tolerant, and that our solutions yield makespan improvements in such scenarios. In addition, we propose a dynamic task clustering strategy to optimize the workflow’s makespan by dynamically adjusting the clustering granularity when failures arise. A trace-based simulation of five real workflows shows that our dynamic method is able to adapt to unexpected behaviors, and yields better makespans when compared to static methods.