Assessment of various bias correction methods and future projection of minimum and maximum temperatures using regional climate model over Thanjavur district

The utilisation of the regional climate models in climate change impact assessments is challenging owing to the threat of bias. Prior to using RCM simulations for developing future climate scenarios, some corrections must be performed. This study aims to evaluate the performance of the bias correction techniques (linear scaling, delta change, variance scaling and distribution mapping methods) using CORDEX (Coordinated Regional Climate Downscaling Experiment) simulated temperature datasets over Thanjavur district. Various statistical metrics are used to assess the performance of the bias correction methods against the observed temperature data. After bias correction, all methods greatly improved the raw RCM estimations. However, the distribution mapping exhibits good agreement than others since it corrects average, standard deviation and quantiles. The future minimum and maximum temperatures are projected from 2025 to 2100 under both RCP4.5 and 8.5 scenarios. Comparing with observed data, the results show that in the twenty-first century, the annual mean minimum temperatures are expected to rise about 1.06–1.38 °C, 1.58–2.12 °C, 1.99–2.32 °C (RCP4.5), and 1.59–2.16 °C, 2.65–2.71 °C, 3.73–3.87 °C (RCP8.5) in the near, mid, and far ranges, respectively. Also, the annual mean maximum temperatures are estimated to rise by around 0.54–1.11 °C, 0.91–1.07 °C, 0.64–1.42 °C (RCP4.5), and 0.67–1.25 °C, 1.41–1.63 °C, 2.28–2.51 °C (RCP8.5) in the near, mid, and far ranges, respectively. Due to the projected higher temperatures in both RCP scenarios, the state’s agricultural production, food security, ecosystems and the environment would be affected, and acute weather events such as heat waves and droughts would occur more frequently with higher severity. Thus, this study provides unambiguous details on future temperatures to the environmentalists, policy-makers of water resource and disaster management.