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A mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined. Due to limitations in computing power which necessitates coarse spatial resolution, climate models do not include many details on mountains and their impact on precipitation. For this reason, it is difficult to estimate the impact of climate change on the availability of water for human consumption in places like the western United States, where mountain snowpack is an important source of water. This paper presents a way to adjust precipitation estimates from climate models by using some simple statistics about nearby mountains and valleys. The adjusted precipitation is then used in a hydrologic model to calculate the runoff that is simulated with different precipitation inputs. Results show that in most cases, the precipitation adjustment improves estimates of the resultant runoff relative to simulations with observed precipitation. The main exceptions are in very dry areas where the climate model produces far too much precipitation. With further work, the proposed adjustment may be integrated into the climate model itself to make it easier for those managing water resources (e.g., controlling reservoir levels) to use the model output to plan and adapt to climate change. A mass‐conserving method for downscaling orographic precipitation improves modeled runoff from the CESM2Considering upwind topography further improves modeled runoff compared to simpler adjustmentsNot tuning to individual model grid points makes this method more generalizable than many existing statistical downscaling methods A mass‐conserving method for downscaling orographic precipitation improves modeled runoff from the CESM2 Considering upwind topography further improves modeled runoff compared to simpler adjustments Not tuning to individual model grid points makes this method more generalizable than many existing statistical downscaling methods

In‐situ heating experiments have been conducted at temperatures of approximately 1200 K utilising a new design of scanning electron microscope, the Cam‐Scan X500. The X500 has been designed to optimise the potential for electron backscatter diffraction (EBSD) analysis with concomitant in‐situ heating experimentation. Features of the new design include an inclined field emission gun (FEG) column, which affords the EBSD geometrical requirement of a high (typically 160°) angle between the incoming electron beam and specimen surface, but avoids complications in heating‐stage design and operation by maintaining it in a horizontal orientation. Our studies have found that secondary electron and orientation contrast imaging has been possible for a variety of specimen materials up to a temperature of at least 900°C, without significant degradation of imaging quality. Electron backscatter diffraction patterns have been acquired at temperatures of at least 900°C and are of sufficient quality to allow automated data collection. Automated EBSD maps have been produced at temperatures between 200°C and 700°C in aluminium, brass, nickel, steel, quartz, and calcite, and even at temperatures >890°C in pure titanium. The combination of scanning electron microscope imaging techniques and EBSD analysis with high‐temperature in‐situ experiments is a powerful tool for the observation of dynamic crystallographic and microstructural processes in metals, semiconductor materials, and ceramics.