## 5 Conclusions  
The bias correction of the GCM-simulated output data is an important step before they can be used for the investigation of the impacts of climate change at the regional scale, because of the existence of biases in the model-simulated data. In this study, four bias correction techniques for precipitation, i.e., Linear Scaling, Local Intensity Scaling, Power Transformation, and Distribution mapping methods, and three bias correction techniques for temperature, i.e., Linear Scaling, Variance Scaling and Distribution Mapping methods, are applied to the CMIP5 ACCESS1-3 model simulation daily precipitation data from 1979 to 2005. For precipitation, the K-S test assessment index D value in each month is maximum in the model-simulated uncorrected data series with respect to the observed data, and the results of the K-S test are improved after applying all four correction techniques. Among all four methods, the DM method gives better results, as the D value is lowest in the DM method each month compared to the other methods. Spatial variation analysis also reveals that the uncorrected simulated data is not sufficiently accurate for use in climate change studies on a watershed scale because of its coarser spatial resolution and the presence of biases within it. Spatial variation maps show that the uncorrected model data underestimate the annual average precipitation data as they show the lowest value of precipitation compared to that of the observed and bias-corrected data. It seems that the spatial variation pattern of all the methods is similar except for the DM method, which matches with the observed data. Hence, in general, the  
Figure 11: Spatial variation of average daily minimum temperature (\({}^{\circ}\)C) for (**a**) model-simulated raw data, (**b**) observed data, (**c**) LS-corrected, (**d**) VS-corrected, and (**e**) DM method. DM method resulted in greater improvements to the D and \(p\)-values in comparison to the other methods. Hence, we conclude that the DM method is a better bias correction method for daily precipitation data than other methods. Similarly, for maximum and minimum temperature, all three correction techniques improved the K-S test assessment index D and significance \(p\)-value. In this case, based on the statistics estimated by the K-S test, the DM method also gives better results in comparison to the LS and VS methods. The spatial variation of the corrected maximum and minimum temperature shows a similar pattern of variation to all the methods with the observed data. But the case of the uncorrected simulated data of the minimum temperature shows conspicuous results in the Western Ghat region in the study area. Hence, after overall statistical analysis and study, we conclude that the DM method is a better bias correction method for daily maximum and minimum temperature data in comparison to the LS, LOCI, PT, and VS methods. Although this work showed that various bias correction approaches that were evaluated for correcting the CMIP5-model-simulated daily precipitation and temperature data, it did not focus on the efficacy of the specific bias correction method in replicating the hydrological extremes. Hence, the future scope of this work could include an assessment the effectiveness of the bias correction methods in terms of extreme precipitation and temperature events. The future scope of the study will also concentrate on the various distribution-based and spatial disaggregation bias correction techniques, which are critical for developing the most effective climate information system for the sub-basin. The new bias correction methods may be developed by addressing extreme events, spatial heterogeneity and non-stationarity in biases, ensemble-based bias correction methods, and multivariate bias correction methods, by considering the interaction between precipitation and temperature which influences the regional climate conditions.