The complexity of the Earth system, the intrinsic variability of its processes and the limited knowledge about the considered sub-systems demand a careful quality assurance of the findings that are drawn from modeling studies. In particular, quantification of uncertainty and identification of sensitive processes, parameters or initial values are of continuous importance when climate (impact) phenomena are studied by experimenting with simulation models. The multi-run simulation environment SimEnv has been developed to support modelers and analysts who are confronted with such issues. The focal point of SimEnv is set on the usage and evaluation of simulation models mainly for quality assurance matters and scenario analyses. SimEnv supports sensitivity and uncertainty analyses of models with large and multi-dimensional output in high-dimensional model factor (parameter / initial values) spaces. Interfacing models to the simulation environment is based on minimal source code modification by calling SimEnv functions. Actually, 10 model programming languages and shell scripts are supported. Generic experiment types are the backbone of SimEnv, applying probabilistic, deterministic or Bayesian numerical sampling schemes in factor spaces. The resulting multi-run experiment can be distributed on a compute cluster or on a multi-core machine. Interactive experiment post-processing makes use of built-in operators, optionally supplemented by user-defined and composed operators. Operator chains can be applied to experiment output, external reference data or output from other SimEnv experiments to navigate and post-process in the combined sample and model output space. Derived multi-dimensional post-processor model quality measures can be evaluated by the visualization framework SimEnvVis using advanced visualization techniques.