# Projections of Climate Change Impact on Stream Temperature: A National-Scale Assessment for Poland
## 2 Materials and Methods
### Climate Change Scenarios  
Future climate scenarios were formulated using data derived from EURO-CORDEX projections, as outlined by [PERSON] et al. [43] following the guidelines of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The EURO-CORDEX framework provides high-resolution climate projections that are particularly suitable for regional-scale studies in Europe. The simulations, obtained through the downscaling of global climate projections CMIP5, covered the European domain at a resolution of 12.5 km on a regular grid (0.11\({}^{\circ}\)). Recognizing the inherent uncertainty in future climate changes, the analysis focused on two emission scenarios, namely RCP4.5 and RCP8.5, illustrating potential increases in greenhouse gas concentrations in the atmosphere. Descriptions and underlying assumptions of both RCP scenarios were detailed in [PERSON] et al. 's work [44]. Although not directly aligning with the latest Shared Socioeconomic Pathways (SSPs) presented in the IPCC's 2022 report, these RCP scenarios offered a comparable range of possible emission trajectories. Specifically, the RCP4.5 scenario corresponds to the medium-low emissions trajectory SSP2 (Middle of the Road), while the RCP8.5 scenario aligns with the high-emissions trajectory SSP5 (Fossil-fueled Development) within the SSP framework. The preparation of climatic data, encompassing variables such as temperature, precipitation, wind speed, relative humidity, and solar radiation, has been extensively detailed in the work of [PERSON] et al. [45]. For the purposes of this study, a concise overview will be provided with a specific focus on temperature due to its pivotal role in the current research context. In the designated study area, we chose six EURO-CORDEX regional climate models (Table 1) to construct a comprehensive ensemble of climate projections for each emission scenario and every relevant climate variable. The analytical framework was executed at a daily temporal resolution, spanning a significant 95-year periodfrom 2006 to 2100. The selected climate variables for examination comprised the minimum and maximum 2 m temperature (expressed in Kelvin). To enhance the reliability of each model, we implemented a systematic bias reduction through a statistical correction method. Specifically, we employed a non-parametric quantile mapping approach, known as robust empirical quantiles (RQUANTs), recognized for its effectiveness in addressing non-continuous climate parameters like precipitation, as evidenced in studies such as that by [PERSON] et al. [46]. This technique utilizes a local linear square regression function to estimate the quantile-quantile relation values between observed and modelled time series, specifically for regularly spaced quantiles. The quantile mapping procedure was applied to temperature grid cells independently within the analyzed domain. To facilitate statistical correction, observation data matching the spatial and temporal resolution of EURO-CORDEX products were utilized. Ground monitoring stations operated by the Polish National Hydrological and Meteorological Service, Institute of Meteorology and Water Management--Polish Research Institute (IMGW-PIB), were used. The observation sets, crucial for the training period of RQUANT data fitting, spanned a duration of 10 years, from 2006 to 2015. Ground station data were interpolated to the Polish territory, established as part of Action COST VALUE, utilizing a linear radial basis function, resulting in a spatial resolution of 0.11\({}^{\circ}\). A preview of this subdomain is accessible on the COST VALUE official webpage ([[http://www.value-cost.eu/WG2_griddedobs](http://www.value-cost.eu/WG2_griddedobs)]([http://www.value-cost.eu/WG2_griddedobs](http://www.value-cost.eu/WG2_griddedobs)) (accessed on 21 July 2022)). In the final step, temperature values were averaged over SWAT sub-basins before being incorporated into the model.