On 2 July 2016 afternoon a heavy rainfall (52.8 mm) was observed over southwestern part of Armenia, at Talin station. High‐frequency radar observations and Weather Research and Forecasting (WRF) model output show that initiation of earliest convection occurred over Aragats mountain massif around noon due to low‐level convergence of thermally induced upslope winds. Further convective development was affected by generation of secondary convection as a result of interaction between cold pool outflows from developed convective cells and upslope winds. The high‐resolution WRF run (3 km) using NSSL two‐moment cloud microphysics parametrization and the European Centre for Medium‐Range Weather Forecasts operational model forcing data best reproduces the location, timing, magnitude, and microphysical structure of the observed convective rainfall among six WRF microphysical schemes tested in this study. Radar observations show that cold cloud process typical for continental‐type deep convection was observed in Armenia. The NSSL includes the double‐moment microphysics schemes for both warm and cold cloud processes, which might be a reason for improved simulation of observed heavy rainfall event in Armenia. Using the coarser resolution ERA5 analysis forcing data in the WRF model leads to simulation of earlier rainfall peaks at Talin station and spurious convective rainfall areas. The WRF model forced by the Global Data Assimilation System Final analysis from the National Centers for Environmental Prediction, even run at 3‐km resolution, is not able to reproduce the accurate location of convection and rainfall over the study area.

Verification of 12-h daily precipitation amount forecasts is considered in Armenia making use of ERA-Interim model output data. The performance of ERA-Interim model is evaluated against observations of 30 meteorological stations of Armenia over the period 1996–2010. Daily precipitation amounts possess high spatio-temporal variability in Armenia associated with complex orography. The verification results have shown that the mentioned variability of precipitation amounts is not reproduced appropriately by forecasted data of ERA-Interim model.

The surface and tropospheric temperature trends have been evaluated over Armenia. A very clear signal of warming is detected at most stations of Armenia for spring, summer and autumn seasons for the period 1979–2012 compared to 1961–1994. Overall, the observed data and the reanalyses products (ERA‐40 and ERA‐Interim) provide a generally consistent picture for tropospheric temperature trends over Armenia. The trends obtained from the three sources of data show significantly faster warming at the near‐surface troposphere (at 850 hPa) for the period 1979–2012 compared to 1961–1994. However, temperature trends at 850 hPa are underestimated by the reanalysis products relative to the observed data. The warming rates decrease with height, and negative and nearly zero trends are observed in the middle and upper troposphere in most cases.

Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi‐scenario, and multi GCM‐ RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature and substantial bottom warming , longer stratification periods (+55 days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems.