Spatial Aggregation of ROC and Precision-Recall Curves

Romain Pic; Zhongwei Zhang; Sebastian Engelke; Johanna Ziegel

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arXiv · 2609.19517

Spatial Aggregation of ROC and Precision-Recall Curves

Romain Pic·Zhongwei Zhang·Sebastian Engelke·Johanna Ziegel

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Abstract

Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are widely used to assess the discrimination ability of forecasts for binary events, such as threshold exceedances or warnings of extreme events. In weather forecasting, forecasts are provided as spatial fields, yielding location-wise ROC and PR curves that are often aggregated to facilitate comparison. However, the effect of the aggregation strategy on performance assessment remains poorly understood. We investigate how different aggregation strategies for ROC and PR curves affect the assessment of discrimination ability. In particular, we identify conditions under which aggregation strategies satisfy two desirable properties for fair comparison: preservation of dominance between forecasts and preservation of concavity or achievability of the curves. We obtain sufficient conditions and propose two strategies satisfying them. They are compared with existing strategies from the literature, and we analyze their properties and highlight potential pitfalls that may lead to misleading interpretations. Based on these findings, we provide practical guidelines for the interpretation of aggregated ROC and PR curves. The proposed framework is illustrated with AI-based global weather forecasts, showing how different aggregation strategies can yield different rankings of competing forecasts.

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BibTeX RIS

Romain Pic, Zhongwei Zhang, Sebastian Engelke, Johanna Ziegel. 2026-09-17. Spatial Aggregation of ROC and Precision-Recall Curves. https://arxiv.org/abs/2609.19517

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