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We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM)\udusing an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and\udan analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature,\udprecipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land\udsurface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the\uddescription and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN\udmeteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are\udprovided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the\udALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the\udADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but\udalso snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM\uddata or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various\udlearning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration,\udand the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of\udthe method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air\udtemperature are generally better than for precipitation. Results in terms of snow depth are satisfactory, which can be viewed as\udindicating a reasonably good intervariable consistency of the meteorological data produced by the method. In terms of temporal\udtransferability (evaluated over time periods of 15 years only), results depend on the learning period. In terms of RCM grid point\udselection technique, the use of a complex RCM grid points selection technique, taking into account horizontal but also altitudinal\udproximity to SAFRAN massif centre points/altitude couples, generally degrades evaluation metrics for high altitudes compared to\uda simpler grid point selection method based on horizontal distance

The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models