Globally, the impacts of climate change can vary across different regions. This study uses a probability framework to evaluate recent historical (1976–2016) and near-future projected (until 2049) climate change across Europe using Climate Research Unit and ensemble climate model datasets (under RCPs 2.6 and 8.5). A historical assessment shows that although the east and west of the domain experienced the largest and smallest increase in temperature, changes in precipitation are not as smooth as temperature. Results indicate that the maximum changes between distributions of the variables (temperature and precipitation) mainly occur at extreme percentiles (e.g., 10% and 90%). A group analysis of four subregions of Europe, namely east (G1), north (G2), west/south (G3), and UK/Ireland (G4), shows that G1 and G4 are expected to have the largest increase in temperature and precipitation extremes, respectively. Although maximum increases in temperature in G3 and G4 occur at larger percentiles, G1 and G2 experience maximum increases at both large and small percentiles. The maximum increase of precipitation over the study domain, however, occurs mainly at larger extremes. To quantify changes in the distribution of projection (2020–2049) relative to the historical reference (1976–2005), two measures are defined, namely a change in occurrences (KS statistic) and intensities at different quantiles (Δ). Results confirm that the temperature distribution tends to shift to higher temperatures. Changes in distribution and extremes of precipitation are spatially variable. Furthermore, extremes are expected to be intensified under RCP 8.5. The quantile analysis and defined measures reveal changes in the entire probability distribution, reflecting possible climate changes in the future.

Estimating precipitation at high spatial-temporal resolution is vital in manifold hydrological, meteorological and water management applications, especially over areas with un-gauged networks and regions where water resources are on the wane. This study aims to evaluate five downscaling methods to determine the accuracy and efficiency of which on generating high-resolution precipitation data at annual and monthly scales. To establish precipitation-Land surface characteristics relationship, environmental factors, including Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Digital Elevation Model (DEM), were considered as proxies in the spatial downscaling procedure. The downscaling algorithms, namely support vector machine (SVM), random forest (RF), geographically weighted regression (GWR), multiple linear regression (MLR) and exponential regression (ER), were implemented to downscale the version 7 of TRMM (Tropical Rainfall Measuring Mission) precipitation (3B43 V7 product) over Lake Urmia Basin (LUB) from 0.25° to 1 km spatial resolution. The downscaled precipitation data was validated against observations from meteorological stations. Monthly fractions derived from TRMM 3B43 were used to disaggregate 1 km annual precipitation to 1 km monthly precipitation. Furthermore, the best method was selected for calibration based on Geographical Difference Analysis (GDA) to assess the effectiveness of the calibration as a viable option. The results indicate that SVM not only outperforms the other methods, but also has good agreements with in-situ measurements compared to the original TRMM. The results confirm that inclusion of LST and geographic information along with NDVI can improve the downscaling performance. Downscaling and GDA calibration significantly improve the accuracy of TRMM 3B43 product at both spatial and temporal resolution and should be considered as an essential step in calibration of TRMM precipitation. Calibration at monthly scale yields slightly better results than calibration at annual scale and then disaggregating into monthly maps in terms of accuracy assessment.