Why I don't need an Overlap between Observation and SSP/RCP data in Statistical downscaling?

For implementing statistical downscaling of General Circulation Model (GCM) data, it's not essential to have an overlap between Shared Socioeconomic Pathway (SSP) data and observational data. Instead, the critical factor is ensuring an overlap between the historical data of the models and the observation data. The Statistical Downscaling (SD) method aims to establish a relationship between observational and historical model data, often through techniques such as Cumulative Distribution Function (CDF) matching. Once this relationship is established, it can be applied to SSP data to downscale and refine the projections, providing more localized and detailed information on future climate conditions.

The nature of observation data corresponds closely with historical data, whereas it diverges from Shared Socioeconomic Pathway (SSP) data. SSP data is scenario-driven, forecasting changes such as temperature rises based on specific narratives. Therefore, conducting a direct comparison between observation and SSP data may not be appropriate.

In statistical downscaling, the overlap between observation and Shared Socioeconomic Pathways (SSP) data is not essential. This is because statistical downscaling techniques primarily compare observation data with the historical data of models, rather than directly with SSP data.

Statistical downscaling seeks to establish statistical relationships between large-scale climate variables, typically provided by Global Climate Models (GCMs) under the category of Historical data, and local-scale climate variables, represented by observed climate data. These relationships are subsequently utilized to downscale GCM projections to local scales, offering more detailed and localized climate information.

Observation data serves a crucial role in calibrating and validating statistical downscaling models. The historical period of General Circulation Model (GCM) data, which aligns with the observation period, is indispensable for establishing these statistical relationships. However, Shared Socioeconomic Pathway (SSP) data represents future socioeconomic scenarios and is employed to project future climate conditions based on these scenarios.

Given that Shared Socioeconomic Pathway (SSP) data delineates hypothetical future scenarios, it is not directly juxtaposed with observation data in statistical downscaling. Instead, statistical downscaling models are fine-tuned and authenticated using historical General Circulation Model (GCM) data alongside corresponding observation data. Following validation, these models can be utilized to downscale forthcoming GCM projections grounded on SSPs, obviating the necessity for direct congruence between SSP and observation data.

Bias Correction on Monthly CMIP6 Data - CanESM5