A review of spatially explicit climate emulators for enhancing modelling agility

Machine-learning-based weather and climate emulators are rapidly transforming how climate information is generated and applied by enabling fast scenario exploration, large ensemble analysis, and the generation of decision-relevant climate data at scales beyond the reach of traditional climate models. Emulators are increasingly integrated into policy-relevant assessments and are expected to play a growing role in upcoming IPCC reports. Yet the field remains fragmented as task definitions and evaluation standards differ across communities, and frameworks for connecting short-term weather emulation to long-term climate projections are missing..

Here, we synthesise 77 studies on spatially explicit climate, hybrid weather-climate, and weather emulators within a unified conceptual framework, mapping inputs and outputs, methodological choices, validation practices, and computational requirements. Three structural patterns emerge. First, most climate emulators prioritise computational speed and scenario agility but offer limited output flexibility, typically generating gridded fields for a narrow set of variables. Second, the emulator landscape is fragmented: weather and hybrid weather-climate emulators form a coherent, machine-learning-driven cluster, whereas climate emulators are more heterogeneous, less connected to machine-learning advances, and validated inconsistently. Third, state-of-the-art weather emulators often rely on specialised hardware and institutional resources concentrated in a few organisations, raising questions of computational equity and “agility for whom”.

Our findings suggest that realizing genuine agility will require future research to focus on user-tailored outputs, rigorous evaluation across forcing scenarios, cross-domain methodological integration, and equitable access to computational resources. These priorities will help the field transition from methodological innovation toward policy-relevant application.