Global hydrological models (GHMs) are widely used in climate change impact assessments to estimate future changes in <span>freshwater availability, droughts, and floods. Their suitability is often evaluated by comparing simulated daily or monthly </span><span>streamflow with historical observations. However, since climate change impacts are usually expressed as changes relative to a </span><span>reference period, biases in flow magnitude do not necessarily prevent GHMs from accurately representing hydrological change. </span></p> <p>Here, we propose an alternative method for evaluating the suitability of GHMs for quantifying the impact of climate change <span>on water resources. This method is based on the assumption that GHMs that are more effective in capturing the timing and </span><span>magnitude of the annual streamflow anomaly during a historical period (which is mainly caused by climate variability) are </span><span>likely to produce more plausible hydrological responses to climate change. </span><span>Therefore, it compares simulated and observed </span><span>absolute and relative streamflow anomalies instead of streamflow magnitudes themselves; it considers annually aggregated </span><span>streamflow, which, compared with daily or monthly streamflow, is much less impacted by reservoir operations and human </span><span>water use — two important drivers of streamflow that are difficult to model using GHMs.</span></p> <p>In this study, we test this new <span>evaluation method as an example for three GHMs (H08, MIROC-INTEG-LAND, and WaterGAP2.2e), forced by two climate </span><span>datasets (20CRv3–ERA5 and 20CRv3–W5E5). We use streamflow observations from 589 gauging stations worldwide to eval</span><span>uate the method. Magnitude-based evaluation shows large performance differences among the GHMs, with negative median </span><span>Nash–Sutcliffe Efficiency (NSE) values for the two uncalibrated ones due to large biases. In contrast, all GHMs achieve pos</span><span>itive NSE for annual streamflow anomalies. Regarding relative anomalies, the land surface model MIROC-INTEG-LAND </span><span>performs worst with an NSE of about 0.3, whereas the two water resources models perform very similarly and achieve me</span><span>dian NSE values above 0.5. Both models show very similar correlation, but WaterGAP simulates the standard deviation of the </span><span>absolute and relative annual streamflow anomaly better than H08. The differences in performance between the two climate </span><span>forcings are smaller than the differences among the models. In terms of the ability of GHMs to simulate the years in which </span><span>extreme wet and dry anomalies occur, there is exact agreement between the observed and simulated extreme years at only </span><span>7–13 % of analysis locations. Meanwhile, the observed extreme year is identified among the five most extreme simulated years </span><span>at 40–59 % of analysis locations. MIROC-INTEG-LAND also shows the lowest performance regarding extreme anomalies.</span><span> GHM evaluation based on annual streamflow anomalies, particularly relative anomalies, is suitable for assessing how GHMs </span><span></span><span></span><span>translate annual climate variability and, to a certain extent, climate change into hydrological changes. </span></p> <p>However, the proposed <span>GHM evaluation method does not consider the vegetation response to increased atmospheric CO<sub>2</sub> concentrations and climatic </span><span>changes that may strongly affect the hydrological response to climate change. Therefore, multi-model ensemble assessments </span><span>of hydrological impacts of climate change should include even lower-performing GHMs, provided that these GHMs take the </span><span>vegetation response into account.</span>