Bidirectional Extreme Response Analysis for a Synchronized-Period Evaluation of Multiple Precipitation Datasets

Gridded precipitation products are widely used in hydroclimatic studies, yet their suitability for anomaly-sensitive applications cannot be determined from magnitude-based statistics alone. This study introduces Bidirectional Extreme Response Analysis (BERA), a class-based evaluation framework designed to assess the ability of precipitation datasets to reproduce anomalously dry, near-normal, and anomalously wet monthly conditions. BERA decomposes gauge and product-based precipitation series into calendar-month-specific anomaly classes and quantifies agreement, no-response, false-extreme, and opposite-direction outcomes through a directional agreement matrix. The framework was demonstrated in the Upper Tigris River Catchment in southeastern Türkiye using monthly observations from 11 TSMS gauge stations and six precipitation products: CHIRPS v3.0, GPCP v3.3, ERA5, MSWEP v2.80, CHELSA, and CMIP6 EC-Earth3 over the common 1983–2011 period. Results show that conventional metrics alone provide contradictory product rankings, whereas BERA reveals distinct directional performance differences that are directly relevant to anomaly-sensitive applications. CHELSA achieved the highest overall BERA agreement rate (0.757), followed by GPCP v3.3 (0.705), whereas CMIP6 showed the weakest class reproduction (0.340) and the largest share of opposite-direction responses. Across most products, wet anomalies were reproduced more successfully than dry anomalies, indicating that precipitation deficits remain more difficult to identify reliably. Elevation-based comparisons further showed that high-elevation gauges were associated with weaker magnitude performance, while class-based agreement did not decline monotonically with altitude. Station-level differences further indicate that anomaly-class agreement is influenced by local hydroclimatic variability and gauge–grid representativeness, rather than by elevation alone. These findings show that BERA captures a distinct dimension of precipitation product behavior by separating magnitude errors from directional class misclassification. Because of its flexible classification structure, the framework can also be refined for different levels of anomaly severity or application-specific thresholds, allowing product performance to be interpreted according to the criticality of the intended use.