Climate exerts a fundamental control on ecosystem function, species diversity and distribution. Topographic variability may influence surface climate, through processes operating at a landscape- scale. To quantify and model such influences, the topography of a 72 km(^2) area of complex terrain, (including the Moor House National Nature Reserve in northern England) was analysed at 50 m resolution. A suite of topographic variables was created, including distance relative to the Pennine ridge (dist), and elevation difference between each grid cell and the lowest grid cell within a specified neighbourhood {drain). Automatic weather stations (AWS) were deployed in a series of networks to test hypothetical relationships between landscape and climate. Daily maximum air temperature, daily mean soil temperature and daily potential evapotranspiration can be modelled spatially using a daily lapse rate calculated from the difference between daily observations made at two base stations. On days with a south easterly wind direction, daily mean temperature is estimated as a function of lapse rate and dist; the spatial behaviour of temperature is consistent with a föhn mechanism. Daily minimum temperature is modelled using lapse rate and drain on days with a lapse rate of minimum temperature shallower than -2.03 x 10 C m(^-1), incorporating the effects of katabatic air flow. Daily solar radiation surfaces are estimated by a GIS routine that models interactions between slope and solar geometry and accounts for daily variations in cloudiness and daylight duration. The daily climate surfaces were tested using data measured at a range of AWS locations during different times of year. The accuracy of the daily surfaces is not seasonally-dependent. The spatial climate data are particularly well suited to landscape-scale ecology because the methods account for prevailing topoclimatic constraints and because separate climate surfaces are generated for each day, capturing the high frequency variability characteristic of upland regions.