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This paper analyzes the historical precipitation and temperature trends and the future climate projections with reference to the northern part of Tuscany (Italy). The trends are identified and quantified at monthly and annual scale at gauging stations with data collected for long periods (60–90 years). An ensemble of 13 Regional Climate Models (RCMs), based on two Representative Concentration Pathways (RCP4.5 and RCP8.5), was then used to assess local scale future precipitation and temperature projections and to represent the uncertainty in the results. The historical data highlight a general decrease of the annual rainfall at a mean rate of 22 mm per decade but, in many cases, the tendencies are not statistically significant. Conversely, the annual mean temperature exhibits an upward trend, statistically significant in the majority of cases, with a warming rate of about 0.1 °C per decade. With reference to the model projections and the annual precipitation, the results are not concordant; the deviations between models in the same period are higher than the future changes at medium- (2031–2040) and long-term (2051–2060) and highlight that the model uncertainty and variability is high. According to the climate model projections, the warming of the study area is unequivocal; a mean positive increment of 0.8 °C at medium-term and 1.1 °C at long-term is expected with respect to the reference period (2003–2012) and the scenario RCP4.5; the increments grow to 0.9 °C and 1.9 °C for the RCP8.5. Finally, in order to check the observed climate change signals, the climate model projections were compared with the trends based on the historical data. A satisfactory agreement is obtained with reference to the precipitation; a systematic underestimation of the trend values with respect to the models, at medium- and long-term, is observed for the temperature data. [ABSTRACT FROM AUTHOR]

Summary: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution. [Copyright &y& Elsevier]