It has been widely shown that rainfall‐runoff models often provide poor and biased simulations after a change in climate, but evidence suggests existing models may be capable of better simulations if calibration strategies are improved. Common practice is to use “least squares”‐type objective functions, which focus on hydrological behavior during high flows. However, simulation of a drying climate may require a more balanced consideration of other parts of the flow regime, including mid‐low flows and drier years in the calibration period, as a closer analogue of future conditions. Here we systematically test eight objective functions over 86 catchments and five conceptual model structures in southern and eastern Australia. We focus on performance when evaluated over multiyear droughts. The results show significant improvements are possible compared to least squares calibration. In particular, the Refined Index of Agreement (based on sum of absolute error, not sum of squared error) and a new objective function called the Split KGE (which gives equal weight to each year in the calibration series) give significantly better split‐sample results than least squares approaches. This improvement held for all five model structures, regardless of basin characteristics such as slope, vegetation, and across a range of climatic conditions (e.g., mean precipitation between 500 and 1,500 mm/yr). We recommend future studies to avoid least squares approaches (e.g., optimizing NSE or KGE with no prior transformation on streamflow) and adopt these alternative methods, wherever simulations in a drying climate are required. Plain Language Summary Rainfall‐runoff models are useful tools in water resource planning under climate change. They are commonly used to quantify the impact of changes in climatic variables, such as rainfall, on water availability for human consumption or environmental needs. Many parts of the world are projected to be substantially drier, possibly with threatened water resources. Given the importance of water, reliable tools for understanding future water availability are vital for society. However, literature would suggest that the current generation of rainfall‐runoff models is not reliable when applied in changing climatic conditions, underestimating the sensitivity of runoff to a given change in precipitation. Many hydrologists have assumed deficiencies in the underlying model equations are to blame. However, this paper demonstrates significant improvement without changing model equations, by using a different “objective function.” The objective function defines how the model is “tuned” to observations of river discharge, and this article identifies objective functions that tend to make model simulations more robust when applied in a drying climate. Using these objective functions can improve the accuracy and plausibility of future water availability estimates made for climate change impact studies. Key Points “Least squares” approaches should not be used to calibrate models for a drying climate Sum‐of‐absolute‐error calibration approaches tend to select more robust parameter sets Equally weighting each year in the calibration data tends to make calibration more robust