This study aims to provide new insight on the wheat yield historical response to cli-1 mate processes throughout Spain by using statistical methods. Our data includes observed wheat 2 yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circula- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

Climate variability and change can have important impacts for crop production. Therefore, the aim of this study is to investigate projections of the wheat yield in an increasingly warm climate. To address our objectives, we determined relationships between wheat yield in Spain and large‐scale variables. Partial least squares regression was applied to determine the modes of the climate variables that drive wheat‐yield variability, revealing a significant influence of surface solar radiation. Based on seasonal patterns of solar radiation, we determine models to estimate inter‐annual wheat‐yield variability. We find that the performance of the models based on solar radiation is better than that of earlier studies based on temperatures and precipitation variables. In this way, we use simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to project wheat‐yield trend under warming climate by implementing direct statistical downscaling. The expected range of projected wheat yield trend for 21st century indicates decreases of about 6–8% across Spain. The suggested models could be applied for adaptation and planning.

Climate models project an increase in drought and aridity in many regions in response to greenhouse gas concentrations in the atmosphere. In areas with complex topography, such as the Canary Islands, elevation gradients may play an important role in future changes. Convection-permitting climate simulations driven by data from three global climate models included in the Coupled Model Intercomparison Project (CMIP5) have been performed for the Canary Islands. A significant increase in the duration and severity of drought is projected by the end of the twenty-first century (2070–2099), relative to the recent past (1980–2009), under intermediate and high emissions scenarios. In addition, the percentage of land affected by droughts, on average, would increase considerably, covering up to 96% in the higher elevations, in the business-as-usual scenario. These changes and the increase in aridity are more pronounced at higher altitudes due to a clear dependence of temperature rise as a function of elevation and a substantial decrease in precipitation.

In recent years, there have been increasing efforts to link phenology models with seasonal climate predictions in so-called Decision Support Systems (DSS) to tailor crop management strategies. However, temporal discrepancies between phenology models with temperature data gathered on a daily basis and seasonal forecasting systems providing predictability on monthly scales have limited their use. In this work, we present a novel methodology to use monthly average temperature data in phenology models. Briefly stated, we modelled the timing of the appearance of specific grapevine phenological phases using monthly average temperatures. To do so, we computed the cumulative thermal time (Sf ) and the number of effective days per month (effd). The effd is the number of days in a month on which temperatures would be above the minimum value for development (Tb). The calculation of effd is obtained from a normal probability distribution function derived from historical weather records. We tested the methodology on four experimental plots located in different European countries with contrasting weather conditions and for four different grapevine cultivars. The root mean square deviation (RMSD) ranged from 4 to 7 days for all the phenological phases considered, at all the different sites, and for all the cultivars. Furthermore, the bias of observed vs predicted comparisons was not significantly different when using either monthly mean or daily temperature values to model phenology. This new methodology, therefore, provides an easy and robust way to incorporate monthly temperature data into grapevine phenology models.

<p>Drought is one of the main natural phenomenon that has a significant impact on agricultural, economic and environmental. In the Canary Islands, drought events are of great importance due to overexploitation of water resources.  This study analyses climate change projections in terms of frequency, duration, and severity of future drought with the Weather Research and Forecasting (WRF) model driven by some Coupled Models Intercomparison Project phase 5 (CMIP5) simulations, for three periods, until the end of the century, and for two Representative Concentration Pathways (RCP) scenarios (RCP4.5 and RCP8.5). Projected changes are obtained using two well-established drought indices; the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at two different time scales (3 and 12 months). Extreme drought (wet) events are considered when SPI is lower (higher) than -2 (2). The results show an increase of extreme drought events by the end of this century for both emission scenarios, with respect to the reference period (1980-2009) and an uneven impact of altitude in drought. The threshold of extreme dry (wet) decreases from 14.2 to 11.1 mm/year (367.4 to 290.0 mm/year) at the altitude interval of 0-400 m, and from 11.7 to 0.5 mm/year (1546.2 to 1074.2 mm/year) at elevations higher than 2100 m.</p><p> </p>