Understanding causal relationships in multivariate time series is essential across many scientific domains, especially in climatology and meteorology where complex dependencies drive extreme events. Existing tools often lack intuitive visualization, particularly for heterogeneous Granger causality applied to non-Gaussian data such as time series following exponential distributions. There is a need for an accessible, interpretable tool that helps scientists explore temporal dependencies and uncover causal structure in such settings. We present HMMLVis, an original visualization tool designed for multivariate and heterogeneous Granger causal inference (Heterogeneous Granger causality by Minimum Message Length). The tool supports causal discovery in time series with exponential distributions and offers an intuitive interface for exploring temporal relationships. While HMMLVis is applicable across disciplines dealing with time-series data, this work focuses on its use in climatological and meteorological contexts. We demonstrate HMMLVis on several applications involving meteorological phenomena affecting the upper and lower distributional tails, using datasets from renewable energy (wind and PV), air pollution, and the European Meteorological Network (EUMETNET) post-processing benchmark (EUPPBench) across different temporal horizons. Our results show that HMMLVis successfully recovers known causal relationships and additionally reveals previously unobserved temporal dependencies relevant to the specific cases examined. As an interpretable and user-friendly visualization tool, HMMLVis has strong potential to support climatologists, meteorologists, and other researchers working with complex time-series data. By enabling clearer insight into causal interactions, it contributes to more informed scientific understanding and facilitates knowledge discovery across multiple environmental and atmospheric science applications.

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