## 1 Introduction  
The increasing level of atmospheric CO\({}_{2}\) and consequent global climate change are enhancing the need for assessing the amount of carbon stored by terrestrial ecosystems. Among them, forest ecosystems cover about 40% of the Earth's ice-free land surface and represent a great part of the global carbon stock [1, 2], which must be quantified also in view of the carbon emission trading by countries [3]. Worldwide forests account for about 75% of the carbon stored in terrestrial ecosystems (organic carbon, OC) and approximately 40% of the carbon exchange between the atmosphere and the terrestrial biosphere each year [4].  
It is widely accepted that OC sequestration in forest plants and soils is sensitive to meteorological factors such as air temperature and humidity, rainfall, radiation, _etc._[5]. Consequently, climate changes that are already visible and that are expected to increase in the next decades may play a fundamental role in the capacity of carbon sequestration of forest ecosystems located in vulnerable areas like the Mediterranean basin [3]. A better understanding of the interactions between climate changes and the terrestrial biosphere is therefore crucial in planning future land management options [3].  
Different approaches have been used for such a purpose, including FACE [6, 7, 8, 9], air-soil warming experiments and carbon isotopic techniques [10, 11]. Most of the studies are conducted on a stand scale [e.g., 12] or at a coarse resolution [e.g., 13] to derive general information on forest development and biomass accumulation in future scenarios. Unfortunately, the experimental techniques applied cannot be easily extended to larger spatial and temporal scales. A more comprehensive understanding of change impact in highly heterogeneous Mediterranean areas would require the consideration of both climate and morphological spatial variability. This has stimulated the use of remotely sensed images, which offer the fundamental advantage of being directly applicable to estimate forest production over wide areas for multiyear periods.  
The current work examines a 1-km resolution data-set which includes meteorological measurements and estimates of forest gross primary production (GPP) covering the whole Italian national territory. The spatially distributed forest GPP estimates have been obtained by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. This data-set is statistically analyzed in order to develop multivariate regression models which link the inter-year GPP variations of five forest types to seasonal values of temperature and rainfall. The models obtained are then applied to predict the effects of expected environmental changes (+2 \({}^{\circ}\)C and increased CO\({}_{2}\) concentration).  
The paper is organized as follows. The next section introduces the study area and data utilized. Modified C-Fix is then described together with the statistical methodology applied to quantify the effects of inter-year meteorological variations on forest GPP. The results section first introduces the present and expected future climate conditions for different forest areas. Next, the multivariate regression models developed are described, followed by the possible changes in forest productivity resulting from the considered environmental scenarios. The likely consequences of each scenario are finally discussed together with the main sources of uncertainty introduced.