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

To apply statistical downscaling of GCM data, overlap between SSP data and observations is not necessary. What's crucial is the overlap between historical data of models and observation data. The SD method establishes a relationship between observation and historical data(for example CDF), which is then applied to SSP data.

The type of observation data aligns with historical data and differs from SSP data. SSP data is scenario-based, projecting changes such as temperature increases according to specific patterns. Consequently, a direct comparison between observation and SSP data isn't correct.

In statistical downscaling, the overlap between observation and SSP (Shared Socioeconomic Pathways) data is not required because statistical downscaling techniques do not directly compare observation data with SSP data.

Statistical downscaling aims to establish statistical relationships between large-scale climate variables (such as those provided by GCMs with the name Historical data) and local-scale climate variables (such as observed climate data). These relationships are then used to downscale GCM projections to local scales.

Observation data is used to calibrate and validate statistical downscaling models. The historical period of GCM data, which overlaps with the observation period, is essential for establishing these statistical relationships. However, SSP data represents future socioeconomic scenarios and is used to project future climate conditions based on these scenarios.

Since SSP data represents hypothetical future conditions, it is not directly compared with observation data in statistical downscaling. Instead, statistical downscaling models are calibrated and validated using historical GCM data and corresponding observation data. Once validated, these models can be applied to downscale future GCM projections based on SSPs, without the need for direct overlap between SSP and observation data.

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