The Fire Weather Index (FWI) is an essential multivariate climate index for assessing wildfire risk and the associated impacts of climate change, as it provides a quantitative measure of wildfire danger by integrating different critical near-surface fire-weather variables, namely air temperature, relative humidity, wind speed, and precipitation. FWI calculation depends on instantaneous data representing noon local standard times, which are often unavailable in many climate data repositories – particularly in climate projections. In these instances, a "proxy" of actual FWI is often used, applying the same FWI formulation to daily aggregated values (mean, max, or min), despite known limitations in capturing extremes and temporal dynamics.</p> <p>This study investigates the use of deep learning (DL) models to emulate the reference FWI over the Iberian Peninsula – a predominantly Mediterranean and fire-prone region – using only daily inputs. The emulators are trained and evaluated using ERA5-Land data, which, while not observational ground truth, provides a consistent and high-resolution dataset suitable for controlled inter-comparison. The focus is not on validating FWI against observations, but on assessing the ability of DL models to reproduce the reference FWI more accurately than traditional proxy approaches, using the same input data source.</p> <p>Our results show substantial improvements in spatial accuracy, preservation of temporal sequences, and detection of extreme fire danger events when compared with the corresponding proxy version. Furthermore, after evaluating different combinations of input variables for DL model training, we find that precipitation can be excluded without substantially affecting accuracy – especially at the upper end – an important insight given the challenges climate models face in representing precipitation. These findings highlight the potential of deep learning tools to enhance the usability of FWI in contexts where sub-daily data are unavailable, and set the stage for the emulation of other multivariate climate indices, which are vital for climate impact studies, spatial planning and management, and adaptation decision-making.