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Global climate model (GCM) outputs feature systematic biases that render them\udunsuitable for direct use by impact models, especially for hydrological\udstudies. To deal with this issue, many bias correction techniques have been\uddeveloped to adjust the modelled variables against observations, focusing\udmainly on precipitation and temperature. However, most state-of-the-art\udhydrological models require more forcing variables, in addition to\udprecipitation and temperature, such as radiation, humidity, air\udpressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface\udmodel set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect\udcategories (ECs), based on the magnitude and sensitivity of runoff changes,\udis developed and applied. Our results show that, while globally the largest\udchanges in modelled runoff are caused by precipitation and temperature\udbiases, there are regions where runoff is substantially affected by and/or\udmore sensitive to radiation and humidity. Global maps of bias ECs reveal the\udregions mostly affected by the bias of each variable. Based on our findings,\udfor global-scale applications, bias correction of radiation and humidity, in\udaddition to that of precipitation and temperature, is advised. Finer\udspatial-scale information is also provided, to suggest bias correction of\udvariables beyond precipitation and temperature for regional studies

The effect of GCM biases on global runoff simulations of a land surface model